Reset23/the-stack-v2-processed · Datasets at Hugging Face

aa93ad03fc44012a2b48b86533a29102093f9c58

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/solutions_python/Problem_35/176.py

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[]

no_license

dr-dos-ok/Code_Jam_Webscraper

c06fd59870842664cd79c41eb460a09553e1c80a

26a35bf114a3aa30fc4c677ef069d95f41665cc0

refs/heads/master

2020-04-06T08:17:40.938460

2018-10-14T10:12:47

null

0

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def main(filename): f = open(filename) lines = f.readlines() f.close() outlines = [] NORTH, WEST, EAST, SOUTH = 0, 1, 2, 3 T = int(lines.pop(0)) def get_neighbours(arr, row, col): neighbours = [] if row > 0: neighbours.append((NORTH, arr[row - 1][col])) if col > 0: neighbours.append((WEST, arr[row][col - 1])) if col < W - 1: neighbours.append((EAST, arr[row][col + 1])) if row < H - 1: neighbours.append((SOUTH, arr[row + 1][col])) return neighbours for case in xrange(T): H, W = map(lambda x:int(x), lines.pop(0).split(' ')) alt_map = [] link_map = [] basin_map = [] for i in xrange(H): alt_map.append(map(lambda x:int(x), lines.pop(0).split(' '))) for row in xrange(H): link_map.append([]) for col in xrange(W): neighbours = get_neighbours(alt_map, row, col) if len(neighbours) > 0: min_alt = min(zip(*neighbours)[1]) if min_alt < alt_map[row][col]: flow_to = filter(lambda x:x[1] == min_alt, neighbours) tgt_cell = flow_to[0] if len(flow_to) > 1: min_dir = min(zip(*flow_to)[0]) tgt_cell = filter(lambda x: x[0] == min_dir, flow_to)[0] link_map[row].append(tgt_cell[0]) else: link_map[row].append(-1) else: link_map[row].append(-1) def get_delta_row_col(dir): delta_row = 0 delta_col = 0 if dir == NORTH: delta_row = -1 elif dir == WEST: delta_col = -1 elif dir == EAST: delta_col = 1 elif dir == SOUTH: delta_row = 1 return (delta_row, delta_col) def get_conn(row, col): connected = [] cur_dir = link_map[row][col] if cur_dir != -1: d_row, d_col = get_delta_row_col(cur_dir) connected.append((row + d_row, col + d_col)) link_map[row][col] = -1 neighbours = get_neighbours(link_map, row, col) for dir, link_dir in neighbours: if (3 - dir) == link_dir: d_row, d_col = get_delta_row_col(dir) connected.append((row + d_row, col + d_col)) link_map[row + d_row][col + d_col] = -1 return connected basin_map = list(alt_map) cur_char = 'a' nodes = [] num_accounted = 0 i = 0 j = 0 while num_accounted < H * W: while True: if isinstance(basin_map[i][j], int): nodes.append((i, j)) break j += 1 if j == W: j = 0 i += 1 while len(nodes) > 0: node_row, node_col = nodes.pop(0) basin_map[node_row][node_col] = cur_char num_accounted += 1 for row, col in get_conn(node_row, node_col): nodes.append((row, col)) cur_char = chr(ord(cur_char) + 1) line = 'Case #%i:\n' % ((case + 1)) for row in xrange(H): line += ' '.join(basin_map[row]) line += '\n' outlines.append(line) f = open('B.out', 'w') f.writelines(outlines) f.close() if __name__ == "__main__": main('B-large.in')

[ "miliar1732@gmail.com" ]

miliar1732@gmail.com

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/moodledata/vpl_data/22/usersdata/107/11706/submittedfiles/av1_2.py

1ed072be432d68e735832fde03663c7d7ac9cb0d

[]

no_license

rafaelperazzo/programacao-web

95643423a35c44613b0f64bed05bd34780fe2436

170dd5440afb9ee68a973f3de13a99aa4c735d79

refs/heads/master

2021-01-12T14:06:25.773146

2017-12-22T16:05:45

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# -*- coding: utf-8 -*- from __future__ import division import math a=int(input('digite o valor de a:')) b=int(input('digite o valor de b:')) c=int(input('digite o valor de c':)) d=int(input('digite o valor de d:')) if ABAD==5393 and CBCD==6268: PRINT('VERDADEIRO') ELSE: print('FALSA')

[ "rafael.mota@ufca.edu.br" ]

rafael.mota@ufca.edu.br

4dece2cdb4af6765f620558479f12b10a049bb03

8e3bd35267f40341d7ca03646e10a2b92eace0c7

/series.py

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[]

no_license

shirsho-12/mathScripts

2eb762b64ec61ffe8f0182f478353fda121d8c3b

0ada093050221a2f4d9b33c09783b052c17fbcb3

refs/heads/master

2023-04-01T06:29:55.308901

2021-04-17T13:54:10

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import numpy from sympy import Symbol, pprint, simplify import sympy as sp def get_series(var, expr, num_terms=10): series = sp.series(expr, var, n=num_terms) pprint(simplify(series)) x = Symbol("x") expr = sp.ln(1 - 8*x**2) # expr = sp.cos(x) # expr = sp.atan(x**3) # expr = sp.ln(sp.sec(x)) get_series(x, expr)

[ "shirshajit@gmail.com" ]

shirshajit@gmail.com

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/XsJLwhAddzbxdQqr4_4.py

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[]

no_license

daniel-reich/turbo-robot

feda6c0523bb83ab8954b6d06302bfec5b16ebdf

a7a25c63097674c0a81675eed7e6b763785f1c41

refs/heads/main

2023-03-26T01:55:14.210264

2021-03-23T16:08:01

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""" Create a function that takes a list and returns the **difference** between the biggest and smallest numbers. ### Examples difference_max_min([10, 4, 1, 4, -10, -50, 32, 21]) ➞ 82 # Smallest number is -50, biggest is 32. difference_max_min([44, 32, 86, 19]) ➞ 67 # Smallest number is 19, biggest is 86. ### Notes N/A """ def difference_max_min(lst): ooga = max(lst) booga = min(lst) return ooga - booga

[ "daniel.reich@danielreichs-MacBook-Pro.local" ]

daniel.reich@danielreichs-MacBook-Pro.local

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/TMviewList/TMview/main.py

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[]

no_license

jiangyg/ZWFproject

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refs/heads/master

2020-09-26T17:01:00.229380

2019-11-15T13:16:21

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0

1

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2019-12-06T09:55:37

2019-12-06T09:55:36

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from scrapy import cmdline cmdline.execute('scrapy runspider ./TMview/spiders/tm_view.py'.split())

[ "34021500@qq.com" ]

34021500@qq.com

c8d933b28a46b474602a1ecd35e3973757ca6e7c

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/pardus/playground/kenan/desktop/compiz/compizconfig-python/actions.py

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[]

no_license

aligulle1/kuller

bda0d59ce8400aa3c7ba9c7e19589f27313492f7

7f98de19be27d7a517fe19a37c814748f7e18ba6

refs/heads/master

2021-01-20T02:22:09.451356

2013-07-23T17:57:58

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py

#!/usr/bin/python # -*- coding: utf-8 -*- # # Licensed under the GNU General Public License, version 2. # See the file http://www.gnu.org/licenses/old-licenses/gpl-2.0.txt. from pisi.actionsapi import autotools from pisi.actionsapi import pisitools def setup(): autotools.configure("--disable-static") def build(): autotools.make() def install(): autotools.install() pisitools.dodoc("COPYING")

[ "yusuf.aydemir@istanbul.com" ]

yusuf.aydemir@istanbul.com

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b55f7fe191a0ac499213505b297edffd2daab2ec

/DeepRLTrader/core/__init__.py

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[ "Apache-2.0", "LicenseRef-scancode-warranty-disclaimer" ]

permissive

chmbrs/DeepRLTrader

af77c33aee908d732fa760a1c48273f9b8ec6ae5

96ae2069a42e29838aa26165af0556835c1808dd

refs/heads/master

2020-04-17T00:46:06.199575

2019-01-16T16:51:48

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UTF-8

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py

from .environnement import Local_env from .environnement import Live_env from .worker import Local_Worker from .worker import Live_Worker from .session import Local_session from .session import Live_session

[ "awakeproduction@hotmail.fr" ]

awakeproduction@hotmail.fr

705172c4e9453f453bfc4b37feb291384cd02836

8ad8ee4e3a4e0e8ae0ed8e92c68cf122f5ba3723

/web_JK_ENG00/webScript/UniversityofBolton.py

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[]

no_license

yangyangyanga/automatic_update

5b5065713853c4a1225142ece4ea39be1a05d011

53c1777cbb84e489b887f38e2745477d6b6f4604

refs/heads/master

2020-05-25T21:18:24.979779

2019-05-22T08:34:02

187,996,951

0

null

UTF-8

Python

false

89

py

from middleware import * makeThreading(urllist=getUrl(223),school='University of Bolton')

[ "1102213456@qq.com" ]

1102213456@qq.com

af49cccf78ba0cf24fdf317d6f5fb030c0a9d4a2

b12aa1e2575a0d2b7345be676011afa174394b61

/mengenalMysqlXampp/mengenalMysqlXampp/urls.py

ea5836ac0f652ce7828adad7dfb0ea3c2f209b72

[]

no_license

frestea09/Latihan-django-to-data-scients

52b5968685710cbb7c18525542576d42055b690b

74b93d8daf2342feb69a4725143920eb299e20f8

refs/heads/master

2020-07-10T23:31:05.282827

2019-09-21T03:09:41

204,396,822

0

null

UTF-8

Python

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760

py

"""mengenalMysqlXampp URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path urlpatterns = [ path('admin/', admin.site.urls), ]

[ "ilmanfrasetya@gmail.com" ]

ilmanfrasetya@gmail.com

d635a85034c10c4b59d607191010b1b0900e44c5

97062249c6eb04069c6fb01e71d06bc334c828e1

/desktop/core/ext-py/Mako-0.8.1/mako/codegen.py

fc3469fce50aea72f8ab552c5df346d13597f5c7

[ "MIT", "LicenseRef-scancode-proprietary-license", "LicenseRef-scancode-unknown-license-reference", "Apache-2.0" ]

permissive

Albertsss/hue

1c8b31c64cc420a029f5b5b80712fb3d0c6cbd6e

454d320dd09b6f7946f3cc05bc97c3e2ca6cd485

refs/heads/master

2021-07-08T17:21:13.237871

2018-05-30T06:03:21

135,386,450

0

1

Apache-2.0

2020-07-25T13:36:58

2018-05-30T04:06:18

Python

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Python

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# mako/codegen.py # Copyright (C) 2006-2012 the Mako authors and contributors <see AUTHORS file> # # This module is part of Mako and is released under # the MIT License: http://www.opensource.org/licenses/mit-license.php """provides functionality for rendering a parsetree constructing into module source code.""" import time import re from mako.pygen import PythonPrinter from mako import util, ast, parsetree, filters, exceptions from mako import compat MAGIC_NUMBER = 8 # names which are hardwired into the # template and are not accessed via the # context itself RESERVED_NAMES = set(['context', 'loop', 'UNDEFINED']) def compile(node, uri, filename=None, default_filters=None, buffer_filters=None, imports=None, future_imports=None, source_encoding=None, generate_magic_comment=True, disable_unicode=False, strict_undefined=False, enable_loop=True, reserved_names=frozenset()): """Generate module source code given a parsetree node, uri, and optional source filename""" # if on Py2K, push the "source_encoding" string to be # a bytestring itself, as we will be embedding it into # the generated source and we don't want to coerce the # result into a unicode object, in "disable_unicode" mode if not compat.py3k and isinstance(source_encoding, compat.text_type): source_encoding = source_encoding.encode(source_encoding) buf = util.FastEncodingBuffer() printer = PythonPrinter(buf) _GenerateRenderMethod(printer, _CompileContext(uri, filename, default_filters, buffer_filters, imports, future_imports, source_encoding, generate_magic_comment, disable_unicode, strict_undefined, enable_loop, reserved_names), node) return buf.getvalue() class _CompileContext(object): def __init__(self, uri, filename, default_filters, buffer_filters, imports, future_imports, source_encoding, generate_magic_comment, disable_unicode, strict_undefined, enable_loop, reserved_names): self.uri = uri self.filename = filename self.default_filters = default_filters self.buffer_filters = buffer_filters self.imports = imports self.future_imports = future_imports self.source_encoding = source_encoding self.generate_magic_comment = generate_magic_comment self.disable_unicode = disable_unicode self.strict_undefined = strict_undefined self.enable_loop = enable_loop self.reserved_names = reserved_names class _GenerateRenderMethod(object): """A template visitor object which generates the full module source for a template. """ def __init__(self, printer, compiler, node): self.printer = printer self.last_source_line = -1 self.compiler = compiler self.node = node self.identifier_stack = [None] self.in_def = isinstance(node, (parsetree.DefTag, parsetree.BlockTag)) if self.in_def: name = "render_%s" % node.funcname args = node.get_argument_expressions() filtered = len(node.filter_args.args) > 0 buffered = eval(node.attributes.get('buffered', 'False')) cached = eval(node.attributes.get('cached', 'False')) defs = None pagetag = None if node.is_block and not node.is_anonymous: args += ['**pageargs'] else: defs = self.write_toplevel() pagetag = self.compiler.pagetag name = "render_body" if pagetag is not None: args = pagetag.body_decl.get_argument_expressions() if not pagetag.body_decl.kwargs: args += ['**pageargs'] cached = eval(pagetag.attributes.get('cached', 'False')) self.compiler.enable_loop = self.compiler.enable_loop or eval( pagetag.attributes.get( 'enable_loop', 'False') ) else: args = ['**pageargs'] cached = False buffered = filtered = False if args is None: args = ['context'] else: args = [a for a in ['context'] + args] self.write_render_callable( pagetag or node, name, args, buffered, filtered, cached) if defs is not None: for node in defs: _GenerateRenderMethod(printer, compiler, node) @property def identifiers(self): return self.identifier_stack[-1] def write_toplevel(self): """Traverse a template structure for module-level directives and generate the start of module-level code. """ inherit = [] namespaces = {} module_code = [] self.compiler.pagetag = None class FindTopLevel(object): def visitInheritTag(s, node): inherit.append(node) def visitNamespaceTag(s, node): namespaces[node.name] = node def visitPageTag(s, node): self.compiler.pagetag = node def visitCode(s, node): if node.ismodule: module_code.append(node) f = FindTopLevel() for n in self.node.nodes: n.accept_visitor(f) self.compiler.namespaces = namespaces module_ident = set() for n in module_code: module_ident = module_ident.union(n.declared_identifiers()) module_identifiers = _Identifiers(self.compiler) module_identifiers.declared = module_ident # module-level names, python code if self.compiler.generate_magic_comment and \ self.compiler.source_encoding: self.printer.writeline("# -*- encoding:%s -*-" % self.compiler.source_encoding) if self.compiler.future_imports: self.printer.writeline("from __future__ import %s" % (", ".join(self.compiler.future_imports),)) self.printer.writeline("from mako import runtime, filters, cache") self.printer.writeline("UNDEFINED = runtime.UNDEFINED") self.printer.writeline("__M_dict_builtin = dict") self.printer.writeline("__M_locals_builtin = locals") self.printer.writeline("_magic_number = %r" % MAGIC_NUMBER) self.printer.writeline("_modified_time = %r" % time.time()) self.printer.writeline("_enable_loop = %r" % self.compiler.enable_loop) self.printer.writeline( "_template_filename = %r" % self.compiler.filename) self.printer.writeline("_template_uri = %r" % self.compiler.uri) self.printer.writeline( "_source_encoding = %r" % self.compiler.source_encoding) if self.compiler.imports: buf = '' for imp in self.compiler.imports: buf += imp + "\n" self.printer.writeline(imp) impcode = ast.PythonCode( buf, source='', lineno=0, pos=0, filename='template defined imports') else: impcode = None main_identifiers = module_identifiers.branch(self.node) module_identifiers.topleveldefs = \ module_identifiers.topleveldefs.\ union(main_identifiers.topleveldefs) module_identifiers.declared.add("UNDEFINED") if impcode: module_identifiers.declared.update(impcode.declared_identifiers) self.compiler.identifiers = module_identifiers self.printer.writeline("_exports = %r" % [n.name for n in main_identifiers.topleveldefs.values()] ) self.printer.write("\n\n") if len(module_code): self.write_module_code(module_code) if len(inherit): self.write_namespaces(namespaces) self.write_inherit(inherit[-1]) elif len(namespaces): self.write_namespaces(namespaces) return list(main_identifiers.topleveldefs.values()) def write_render_callable(self, node, name, args, buffered, filtered, cached): """write a top-level render callable. this could be the main render() method or that of a top-level def.""" if self.in_def: decorator = node.decorator if decorator: self.printer.writeline( "@runtime._decorate_toplevel(%s)" % decorator) self.printer.writelines( "def %s(%s):" % (name, ','.join(args)), # push new frame, assign current frame to __M_caller "__M_caller = context.caller_stack._push_frame()", "try:" ) if buffered or filtered or cached: self.printer.writeline("context._push_buffer()") self.identifier_stack.append( self.compiler.identifiers.branch(self.node)) if (not self.in_def or self.node.is_block) and '**pageargs' in args: self.identifier_stack[-1].argument_declared.add('pageargs') if not self.in_def and ( len(self.identifiers.locally_assigned) > 0 or len(self.identifiers.argument_declared) > 0 ): self.printer.writeline("__M_locals = __M_dict_builtin(%s)" % ','.join([ "%s=%s" % (x, x) for x in self.identifiers.argument_declared ])) self.write_variable_declares(self.identifiers, toplevel=True) for n in self.node.nodes: n.accept_visitor(self) self.write_def_finish(self.node, buffered, filtered, cached) self.printer.writeline(None) self.printer.write("\n\n") if cached: self.write_cache_decorator( node, name, args, buffered, self.identifiers, toplevel=True) def write_module_code(self, module_code): """write module-level template code, i.e. that which is enclosed in <%! %> tags in the template.""" for n in module_code: self.write_source_comment(n) self.printer.write_indented_block(n.text) def write_inherit(self, node): """write the module-level inheritance-determination callable.""" self.printer.writelines( "def _mako_inherit(template, context):", "_mako_generate_namespaces(context)", "return runtime._inherit_from(context, %s, _template_uri)" % (node.parsed_attributes['file']), None ) def write_namespaces(self, namespaces): """write the module-level namespace-generating callable.""" self.printer.writelines( "def _mako_get_namespace(context, name):", "try:", "return context.namespaces[(__name__, name)]", "except KeyError:", "_mako_generate_namespaces(context)", "return context.namespaces[(__name__, name)]", None, None ) self.printer.writeline("def _mako_generate_namespaces(context):") for node in namespaces.values(): if 'import' in node.attributes: self.compiler.has_ns_imports = True self.write_source_comment(node) if len(node.nodes): self.printer.writeline("def make_namespace():") export = [] identifiers = self.compiler.identifiers.branch(node) self.in_def = True class NSDefVisitor(object): def visitDefTag(s, node): s.visitDefOrBase(node) def visitBlockTag(s, node): s.visitDefOrBase(node) def visitDefOrBase(s, node): if node.is_anonymous: raise exceptions.CompileException( "Can't put anonymous blocks inside " "<%namespace>", **node.exception_kwargs ) self.write_inline_def(node, identifiers, nested=False) export.append(node.funcname) vis = NSDefVisitor() for n in node.nodes: n.accept_visitor(vis) self.printer.writeline("return [%s]" % (','.join(export))) self.printer.writeline(None) self.in_def = False callable_name = "make_namespace()" else: callable_name = "None" if 'file' in node.parsed_attributes: self.printer.writeline( "ns = runtime.TemplateNamespace(%r," " context._clean_inheritance_tokens()," " templateuri=%s, callables=%s, " " calling_uri=_template_uri)" % ( node.name, node.parsed_attributes.get('file', 'None'), callable_name, ) ) elif 'module' in node.parsed_attributes: self.printer.writeline( "ns = runtime.ModuleNamespace(%r," " context._clean_inheritance_tokens()," " callables=%s, calling_uri=_template_uri," " module=%s)" % ( node.name, callable_name, node.parsed_attributes.get('module', 'None') ) ) else: self.printer.writeline( "ns = runtime.Namespace(%r," " context._clean_inheritance_tokens()," " callables=%s, calling_uri=_template_uri)" % ( node.name, callable_name, ) ) if eval(node.attributes.get('inheritable', "False")): self.printer.writeline("context['self'].%s = ns" % (node.name)) self.printer.writeline( "context.namespaces[(__name__, %s)] = ns" % repr(node.name)) self.printer.write("\n") if not len(namespaces): self.printer.writeline("pass") self.printer.writeline(None) def write_variable_declares(self, identifiers, toplevel=False, limit=None): """write variable declarations at the top of a function. the variable declarations are in the form of callable definitions for defs and/or name lookup within the function's context argument. the names declared are based on the names that are referenced in the function body, which don't otherwise have any explicit assignment operation. names that are assigned within the body are assumed to be locally-scoped variables and are not separately declared. for def callable definitions, if the def is a top-level callable then a 'stub' callable is generated which wraps the current Context into a closure. if the def is not top-level, it is fully rendered as a local closure. """ # collection of all defs available to us in this scope comp_idents = dict([(c.funcname, c) for c in identifiers.defs]) to_write = set() # write "context.get()" for all variables we are going to # need that arent in the namespace yet to_write = to_write.union(identifiers.undeclared) # write closure functions for closures that we define # right here to_write = to_write.union( [c.funcname for c in identifiers.closuredefs.values()]) # remove identifiers that are declared in the argument # signature of the callable to_write = to_write.difference(identifiers.argument_declared) # remove identifiers that we are going to assign to. # in this way we mimic Python's behavior, # i.e. assignment to a variable within a block # means that variable is now a "locally declared" var, # which cannot be referenced beforehand. to_write = to_write.difference(identifiers.locally_declared) if self.compiler.enable_loop: has_loop = "loop" in to_write to_write.discard("loop") else: has_loop = False # if a limiting set was sent, constraint to those items in that list # (this is used for the caching decorator) if limit is not None: to_write = to_write.intersection(limit) if toplevel and getattr(self.compiler, 'has_ns_imports', False): self.printer.writeline("_import_ns = {}") self.compiler.has_imports = True for ident, ns in self.compiler.namespaces.items(): if 'import' in ns.attributes: self.printer.writeline( "_mako_get_namespace(context, %r)."\ "_populate(_import_ns, %r)" % ( ident, re.split(r'\s*,\s*', ns.attributes['import']) )) if has_loop: self.printer.writeline( 'loop = __M_loop = runtime.LoopStack()' ) for ident in to_write: if ident in comp_idents: comp = comp_idents[ident] if comp.is_block: if not comp.is_anonymous: self.write_def_decl(comp, identifiers) else: self.write_inline_def(comp, identifiers, nested=True) else: if comp.is_root(): self.write_def_decl(comp, identifiers) else: self.write_inline_def(comp, identifiers, nested=True) elif ident in self.compiler.namespaces: self.printer.writeline( "%s = _mako_get_namespace(context, %r)" % (ident, ident) ) else: if getattr(self.compiler, 'has_ns_imports', False): if self.compiler.strict_undefined: self.printer.writelines( "%s = _import_ns.get(%r, UNDEFINED)" % (ident, ident), "if %s is UNDEFINED:" % ident, "try:", "%s = context[%r]" % (ident, ident), "except KeyError:", "raise NameError(\"'%s' is not defined\")" % ident, None, None ) else: self.printer.writeline( "%s = _import_ns.get(%r, context.get(%r, UNDEFINED))" % (ident, ident, ident)) else: if self.compiler.strict_undefined: self.printer.writelines( "try:", "%s = context[%r]" % (ident, ident), "except KeyError:", "raise NameError(\"'%s' is not defined\")" % ident, None ) else: self.printer.writeline( "%s = context.get(%r, UNDEFINED)" % (ident, ident) ) self.printer.writeline("__M_writer = context.writer()") def write_source_comment(self, node): """write a source comment containing the line number of the corresponding template line.""" if self.last_source_line != node.lineno: self.printer.writeline("# SOURCE LINE %d" % node.lineno) self.last_source_line = node.lineno def write_def_decl(self, node, identifiers): """write a locally-available callable referencing a top-level def""" funcname = node.funcname namedecls = node.get_argument_expressions() nameargs = node.get_argument_expressions(include_defaults=False) if not self.in_def and ( len(self.identifiers.locally_assigned) > 0 or len(self.identifiers.argument_declared) > 0): nameargs.insert(0, 'context.locals_(__M_locals)') else: nameargs.insert(0, 'context') self.printer.writeline("def %s(%s):" % (funcname, ",".join(namedecls))) self.printer.writeline( "return render_%s(%s)" % (funcname, ",".join(nameargs))) self.printer.writeline(None) def write_inline_def(self, node, identifiers, nested): """write a locally-available def callable inside an enclosing def.""" namedecls = node.get_argument_expressions() decorator = node.decorator if decorator: self.printer.writeline( "@runtime._decorate_inline(context, %s)" % decorator) self.printer.writeline( "def %s(%s):" % (node.funcname, ",".join(namedecls))) filtered = len(node.filter_args.args) > 0 buffered = eval(node.attributes.get('buffered', 'False')) cached = eval(node.attributes.get('cached', 'False')) self.printer.writelines( # push new frame, assign current frame to __M_caller "__M_caller = context.caller_stack._push_frame()", "try:" ) if buffered or filtered or cached: self.printer.writelines( "context._push_buffer()", ) identifiers = identifiers.branch(node, nested=nested) self.write_variable_declares(identifiers) self.identifier_stack.append(identifiers) for n in node.nodes: n.accept_visitor(self) self.identifier_stack.pop() self.write_def_finish(node, buffered, filtered, cached) self.printer.writeline(None) if cached: self.write_cache_decorator(node, node.funcname, namedecls, False, identifiers, inline=True, toplevel=False) def write_def_finish(self, node, buffered, filtered, cached, callstack=True): """write the end section of a rendering function, either outermost or inline. this takes into account if the rendering function was filtered, buffered, etc. and closes the corresponding try: block if any, and writes code to retrieve captured content, apply filters, send proper return value.""" if not buffered and not cached and not filtered: self.printer.writeline("return ''") if callstack: self.printer.writelines( "finally:", "context.caller_stack._pop_frame()", None ) if buffered or filtered or cached: if buffered or cached: # in a caching scenario, don't try to get a writer # from the context after popping; assume the caching # implemenation might be using a context with no # extra buffers self.printer.writelines( "finally:", "__M_buf = context._pop_buffer()" ) else: self.printer.writelines( "finally:", "__M_buf, __M_writer = context._pop_buffer_and_writer()" ) if callstack: self.printer.writeline("context.caller_stack._pop_frame()") s = "__M_buf.getvalue()" if filtered: s = self.create_filter_callable(node.filter_args.args, s, False) self.printer.writeline(None) if buffered and not cached: s = self.create_filter_callable(self.compiler.buffer_filters, s, False) if buffered or cached: self.printer.writeline("return %s" % s) else: self.printer.writelines( "__M_writer(%s)" % s, "return ''" ) def write_cache_decorator(self, node_or_pagetag, name, args, buffered, identifiers, inline=False, toplevel=False): """write a post-function decorator to replace a rendering callable with a cached version of itself.""" self.printer.writeline("__M_%s = %s" % (name, name)) cachekey = node_or_pagetag.parsed_attributes.get('cache_key', repr(name)) cache_args = {} if self.compiler.pagetag is not None: cache_args.update( ( pa[6:], self.compiler.pagetag.parsed_attributes[pa] ) for pa in self.compiler.pagetag.parsed_attributes if pa.startswith('cache_') and pa != 'cache_key' ) cache_args.update( ( pa[6:], node_or_pagetag.parsed_attributes[pa] ) for pa in node_or_pagetag.parsed_attributes if pa.startswith('cache_') and pa != 'cache_key' ) if 'timeout' in cache_args: cache_args['timeout'] = int(eval(cache_args['timeout'])) self.printer.writeline("def %s(%s):" % (name, ','.join(args))) # form "arg1, arg2, arg3=arg3, arg4=arg4", etc. pass_args = [ '=' in a and "%s=%s" % ((a.split('=')[0],)*2) or a for a in args ] self.write_variable_declares( identifiers, toplevel=toplevel, limit=node_or_pagetag.undeclared_identifiers() ) if buffered: s = "context.get('local')."\ "cache._ctx_get_or_create("\ "%s, lambda:__M_%s(%s), context, %s__M_defname=%r)" % \ (cachekey, name, ','.join(pass_args), ''.join(["%s=%s, " % (k, v) for k, v in cache_args.items()]), name ) # apply buffer_filters s = self.create_filter_callable(self.compiler.buffer_filters, s, False) self.printer.writelines("return " + s, None) else: self.printer.writelines( "__M_writer(context.get('local')." "cache._ctx_get_or_create("\ "%s, lambda:__M_%s(%s), context, %s__M_defname=%r))" % (cachekey, name, ','.join(pass_args), ''.join(["%s=%s, " % (k, v) for k, v in cache_args.items()]), name, ), "return ''", None ) def create_filter_callable(self, args, target, is_expression): """write a filter-applying expression based on the filters present in the given filter names, adjusting for the global 'default' filter aliases as needed.""" def locate_encode(name): if re.match(r'decode\..+', name): return "filters." + name elif self.compiler.disable_unicode: return filters.NON_UNICODE_ESCAPES.get(name, name) else: return filters.DEFAULT_ESCAPES.get(name, name) if 'n' not in args: if is_expression: if self.compiler.pagetag: args = self.compiler.pagetag.filter_args.args + args if self.compiler.default_filters: args = self.compiler.default_filters + args for e in args: # if filter given as a function, get just the identifier portion if e == 'n': continue m = re.match(r'(.+?)($.*$)', e) if m: (ident, fargs) = m.group(1,2) f = locate_encode(ident) e = f + fargs else: x = e e = locate_encode(e) assert e is not None target = "%s(%s)" % (e, target) return target def visitExpression(self, node): self.write_source_comment(node) if len(node.escapes) or \ ( self.compiler.pagetag is not None and len(self.compiler.pagetag.filter_args.args) ) or \ len(self.compiler.default_filters): s = self.create_filter_callable(node.escapes_code.args, "%s" % node.text, True) self.printer.writeline("__M_writer(%s)" % s) else: self.printer.writeline("__M_writer(%s)" % node.text) def visitControlLine(self, node): if node.isend: self.printer.writeline(None) if node.has_loop_context: self.printer.writeline('finally:') self.printer.writeline("loop = __M_loop._exit()") self.printer.writeline(None) else: self.write_source_comment(node) if self.compiler.enable_loop and node.keyword == 'for': text = mangle_mako_loop(node, self.printer) else: text = node.text self.printer.writeline(text) children = node.get_children() # this covers the three situations where we want to insert a pass: # 1) a ternary control line with no children, # 2) a primary control line with nothing but its own ternary # and end control lines, and # 3) any control line with no content other than comments if not children or ( compat.all(isinstance(c, (parsetree.Comment, parsetree.ControlLine)) for c in children) and compat.all((node.is_ternary(c.keyword) or c.isend) for c in children if isinstance(c, parsetree.ControlLine))): self.printer.writeline("pass") def visitText(self, node): self.write_source_comment(node) self.printer.writeline("__M_writer(%s)" % repr(node.content)) def visitTextTag(self, node): filtered = len(node.filter_args.args) > 0 if filtered: self.printer.writelines( "__M_writer = context._push_writer()", "try:", ) for n in node.nodes: n.accept_visitor(self) if filtered: self.printer.writelines( "finally:", "__M_buf, __M_writer = context._pop_buffer_and_writer()", "__M_writer(%s)" % self.create_filter_callable( node.filter_args.args, "__M_buf.getvalue()", False), None ) def visitCode(self, node): if not node.ismodule: self.write_source_comment(node) self.printer.write_indented_block(node.text) if not self.in_def and len(self.identifiers.locally_assigned) > 0: # if we are the "template" def, fudge locally # declared/modified variables into the "__M_locals" dictionary, # which is used for def calls within the same template, # to simulate "enclosing scope" self.printer.writeline( '__M_locals_builtin_stored = __M_locals_builtin()') self.printer.writeline( '__M_locals.update(__M_dict_builtin([(__M_key,' ' __M_locals_builtin_stored[__M_key]) for __M_key in' ' [%s] if __M_key in __M_locals_builtin_stored]))' % ','.join([repr(x) for x in node.declared_identifiers()])) def visitIncludeTag(self, node): self.write_source_comment(node) args = node.attributes.get('args') if args: self.printer.writeline( "runtime._include_file(context, %s, _template_uri, %s)" % (node.parsed_attributes['file'], args)) else: self.printer.writeline( "runtime._include_file(context, %s, _template_uri)" % (node.parsed_attributes['file'])) def visitNamespaceTag(self, node): pass def visitDefTag(self, node): pass def visitBlockTag(self, node): if node.is_anonymous: self.printer.writeline("%s()" % node.funcname) else: nameargs = node.get_argument_expressions(include_defaults=False) nameargs += ['**pageargs'] self.printer.writeline("if 'parent' not in context._data or " "not hasattr(context._data['parent'], '%s'):" % node.funcname) self.printer.writeline( "context['self'].%s(%s)" % (node.funcname, ",".join(nameargs))) self.printer.writeline("\n") def visitCallNamespaceTag(self, node): # TODO: we can put namespace-specific checks here, such # as ensure the given namespace will be imported, # pre-import the namespace, etc. self.visitCallTag(node) def visitCallTag(self, node): self.printer.writeline("def ccall(caller):") export = ['body'] callable_identifiers = self.identifiers.branch(node, nested=True) body_identifiers = callable_identifiers.branch(node, nested=False) # we want the 'caller' passed to ccall to be used # for the body() function, but for other non-body() # <%def>s within <%call> we want the current caller # off the call stack (if any) body_identifiers.add_declared('caller') self.identifier_stack.append(body_identifiers) class DefVisitor(object): def visitDefTag(s, node): s.visitDefOrBase(node) def visitBlockTag(s, node): s.visitDefOrBase(node) def visitDefOrBase(s, node): self.write_inline_def(node, callable_identifiers, nested=False) if not node.is_anonymous: export.append(node.funcname) # remove defs that are within the <%call> from the # "closuredefs" defined in the body, so they dont render twice if node.funcname in body_identifiers.closuredefs: del body_identifiers.closuredefs[node.funcname] vis = DefVisitor() for n in node.nodes: n.accept_visitor(vis) self.identifier_stack.pop() bodyargs = node.body_decl.get_argument_expressions() self.printer.writeline("def body(%s):" % ','.join(bodyargs)) # TODO: figure out best way to specify # buffering/nonbuffering (at call time would be better) buffered = False if buffered: self.printer.writelines( "context._push_buffer()", "try:" ) self.write_variable_declares(body_identifiers) self.identifier_stack.append(body_identifiers) for n in node.nodes: n.accept_visitor(self) self.identifier_stack.pop() self.write_def_finish(node, buffered, False, False, callstack=False) self.printer.writelines( None, "return [%s]" % (','.join(export)), None ) self.printer.writelines( # push on caller for nested call "context.caller_stack.nextcaller = " "runtime.Namespace('caller', context, " "callables=ccall(__M_caller))", "try:") self.write_source_comment(node) self.printer.writelines( "__M_writer(%s)" % self.create_filter_callable( [], node.expression, True), "finally:", "context.caller_stack.nextcaller = None", None ) class _Identifiers(object): """tracks the status of identifier names as template code is rendered.""" def __init__(self, compiler, node=None, parent=None, nested=False): if parent is not None: # if we are the branch created in write_namespaces(), # we don't share any context from the main body(). if isinstance(node, parsetree.NamespaceTag): self.declared = set() self.topleveldefs = util.SetLikeDict() else: # things that have already been declared # in an enclosing namespace (i.e. names we can just use) self.declared = set(parent.declared).\ union([c.name for c in parent.closuredefs.values()]).\ union(parent.locally_declared).\ union(parent.argument_declared) # if these identifiers correspond to a "nested" # scope, it means whatever the parent identifiers # had as undeclared will have been declared by that parent, # and therefore we have them in our scope. if nested: self.declared = self.declared.union(parent.undeclared) # top level defs that are available self.topleveldefs = util.SetLikeDict(**parent.topleveldefs) else: self.declared = set() self.topleveldefs = util.SetLikeDict() self.compiler = compiler # things within this level that are referenced before they # are declared (e.g. assigned to) self.undeclared = set() # things that are declared locally. some of these things # could be in the "undeclared" list as well if they are # referenced before declared self.locally_declared = set() # assignments made in explicit python blocks. # these will be propagated to # the context of local def calls. self.locally_assigned = set() # things that are declared in the argument # signature of the def callable self.argument_declared = set() # closure defs that are defined in this level self.closuredefs = util.SetLikeDict() self.node = node if node is not None: node.accept_visitor(self) illegal_names = self.compiler.reserved_names.intersection( self.locally_declared) if illegal_names: raise exceptions.NameConflictError( "Reserved words declared in template: %s" % ", ".join(illegal_names)) def branch(self, node, **kwargs): """create a new Identifiers for a new Node, with this Identifiers as the parent.""" return _Identifiers(self.compiler, node, self, **kwargs) @property def defs(self): return set(self.topleveldefs.union(self.closuredefs).values()) def __repr__(self): return "Identifiers(declared=%r, locally_declared=%r, "\ "undeclared=%r, topleveldefs=%r, closuredefs=%r, "\ "argumentdeclared=%r)" %\ ( list(self.declared), list(self.locally_declared), list(self.undeclared), [c.name for c in self.topleveldefs.values()], [c.name for c in self.closuredefs.values()], self.argument_declared) def check_declared(self, node): """update the state of this Identifiers with the undeclared and declared identifiers of the given node.""" for ident in node.undeclared_identifiers(): if ident != 'context' and\ ident not in self.declared.union(self.locally_declared): self.undeclared.add(ident) for ident in node.declared_identifiers(): self.locally_declared.add(ident) def add_declared(self, ident): self.declared.add(ident) if ident in self.undeclared: self.undeclared.remove(ident) def visitExpression(self, node): self.check_declared(node) def visitControlLine(self, node): self.check_declared(node) def visitCode(self, node): if not node.ismodule: self.check_declared(node) self.locally_assigned = self.locally_assigned.union( node.declared_identifiers()) def visitNamespaceTag(self, node): # only traverse into the sub-elements of a # <%namespace> tag if we are the branch created in # write_namespaces() if self.node is node: for n in node.nodes: n.accept_visitor(self) def _check_name_exists(self, collection, node): existing = collection.get(node.funcname) collection[node.funcname] = node if existing is not None and \ existing is not node and \ (node.is_block or existing.is_block): raise exceptions.CompileException( "%%def or %%block named '%s' already " "exists in this template." % node.funcname, **node.exception_kwargs) def visitDefTag(self, node): if node.is_root() and not node.is_anonymous: self._check_name_exists(self.topleveldefs, node) elif node is not self.node: self._check_name_exists(self.closuredefs, node) for ident in node.undeclared_identifiers(): if ident != 'context' and\ ident not in self.declared.union(self.locally_declared): self.undeclared.add(ident) # visit defs only one level deep if node is self.node: for ident in node.declared_identifiers(): self.argument_declared.add(ident) for n in node.nodes: n.accept_visitor(self) def visitBlockTag(self, node): if node is not self.node and \ not node.is_anonymous: if isinstance(self.node, parsetree.DefTag): raise exceptions.CompileException( "Named block '%s' not allowed inside of def '%s'" % (node.name, self.node.name), **node.exception_kwargs) elif isinstance(self.node, (parsetree.CallTag, parsetree.CallNamespaceTag)): raise exceptions.CompileException( "Named block '%s' not allowed inside of <%%call> tag" % (node.name, ), **node.exception_kwargs) for ident in node.undeclared_identifiers(): if ident != 'context' and \ ident not in self.declared.union(self.locally_declared): self.undeclared.add(ident) if not node.is_anonymous: self._check_name_exists(self.topleveldefs, node) self.undeclared.add(node.funcname) elif node is not self.node: self._check_name_exists(self.closuredefs, node) for ident in node.declared_identifiers(): self.argument_declared.add(ident) for n in node.nodes: n.accept_visitor(self) def visitTextTag(self, node): for ident in node.undeclared_identifiers(): if ident != 'context' and \ ident not in self.declared.union(self.locally_declared): self.undeclared.add(ident) def visitIncludeTag(self, node): self.check_declared(node) def visitPageTag(self, node): for ident in node.declared_identifiers(): self.argument_declared.add(ident) self.check_declared(node) def visitCallNamespaceTag(self, node): self.visitCallTag(node) def visitCallTag(self, node): if node is self.node: for ident in node.undeclared_identifiers(): if ident != 'context' and\ ident not in self.declared.union(self.locally_declared): self.undeclared.add(ident) for ident in node.declared_identifiers(): self.argument_declared.add(ident) for n in node.nodes: n.accept_visitor(self) else: for ident in node.undeclared_identifiers(): if ident != 'context' and\ ident not in self.declared.union(self.locally_declared): self.undeclared.add(ident) _FOR_LOOP = re.compile( r'^for\s+((?:$?)\s*[A-Za-z_][A-Za-z_0-9]*' r'(?:\s*,\s*(?:[A-Za-z_][A-Za-z0-9_]*),??)*\s*(?:$?))\s+in\s+(.*):' ) def mangle_mako_loop(node, printer): """converts a for loop into a context manager wrapped around a for loop when access to the `loop` variable has been detected in the for loop body """ loop_variable = LoopVariable() node.accept_visitor(loop_variable) if loop_variable.detected: node.nodes[-1].has_loop_context = True match = _FOR_LOOP.match(node.text) if match: printer.writelines( 'loop = __M_loop._enter(%s)' % match.group(2), 'try:' #'with __M_loop(%s) as loop:' % match.group(2) ) text = 'for %s in loop:' % match.group(1) else: raise SyntaxError("Couldn't apply loop context: %s" % node.text) else: text = node.text return text class LoopVariable(object): """A node visitor which looks for the name 'loop' within undeclared identifiers.""" def __init__(self): self.detected = False def _loop_reference_detected(self, node): if 'loop' in node.undeclared_identifiers(): self.detected = True else: for n in node.get_children(): n.accept_visitor(self) def visitControlLine(self, node): self._loop_reference_detected(node) def visitCode(self, node): self._loop_reference_detected(node) def visitExpression(self, node): self._loop_reference_detected(node)

[ "540227148@qq.com" ]

540227148@qq.com

5e6b1e4f698c5c092aff75f1caa0e101fcacd01b

f0e8338762530bd6c2cc402cda64c43bcec329ae

/leetcode/35. 搜索插入位置.py

2f9b313fad3d2a95b67f4af9fb389fa870e4f470

[]

no_license

pengyuhou/git_test1

bcd60554d2dadad972848047d00f888444462f05

5aa441f94a0aa713771bdd93b53a702032060f5d

refs/heads/master

2022-11-22T08:52:52.767933

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class Solution(object): def searchInsert(self, nums, target): """ :type nums: List[int] :type target: int :rtype: int """ import bisect return bisect.bisect_left(nums, target) if __name__ == '__main__': # print(Solution().searchInsert([1, 3, 5, 6], 0)) import bisect a = [1, 3, 5, 6] print(bisect.bisect_left(a, 5)) bisect.insort(a,5) print(a)

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/dialogue-engine/test/programytest/parser/template/graph_tests/test_authorise_usergroups.py

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2023-01-12T20:07:34.364188

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""" Copyright (c) 2020 COTOBA DESIGN, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import xml.etree.ElementTree as ET from programy.parser.template.nodes.base import TemplateNode from programy.parser.template.nodes.authorise import TemplateAuthoriseNode from programy.config.brain.brain import BrainConfiguration from programy.config.brain.security import BrainSecurityConfiguration from programytest.parser.template.graph_tests.graph_test_client import TemplateGraphTestClient class TemplateGraphAuthoriseTests(TemplateGraphTestClient): def get_brain_config(self): brain_config = BrainConfiguration() brain_config.security._authorisation = BrainSecurityConfiguration("authorisation") brain_config.security.authorisation._classname = "programy.security.authorise.usergroupsauthorisor.BasicUserGroupAuthorisationService" brain_config.security.authorisation._denied_srai = "ACCESS_DENIED" brain_config.security.authorisation._usergroups = "$BOT_ROOT/usergroups.yaml" return brain_config def test_authorise_with_role_as_attrib_access_allowed(self): template = ET.fromstring(""" <template> <authorise role="root"> Hello </authorise> </template> """) ast = self._graph.parse_template_expression(template) self.assertIsNotNone(ast) self.assertIsInstance(ast, TemplateNode) self.assertIsNotNone(ast.children) self.assertEqual(len(ast.children), 1) auth_node = ast.children[0] self.assertIsNotNone(auth_node) self.assertIsInstance(auth_node, TemplateAuthoriseNode) self.assertIsNotNone(auth_node.role) self.assertEqual("root", auth_node.role) result = auth_node.resolve(self._client_context) self.assertIsNotNone(result) self.assertEqual("Hello", result) def test_authorise_with_role_as_attrib_and_optional_srai_access_allowed(self): template = ET.fromstring(""" <template> <authorise role="root" denied_srai="NO_ACCESS"> Hello </authorise> </template> """) ast = self._graph.parse_template_expression(template) self.assertIsNotNone(ast) self.assertIsInstance(ast, TemplateNode) self.assertIsNotNone(ast.children) self.assertEqual(len(ast.children), 1) auth_node = ast.children[0] self.assertIsNotNone(auth_node) self.assertIsInstance(auth_node, TemplateAuthoriseNode) self.assertIsNotNone(auth_node.role) self.assertEqual("root", auth_node.role) result = auth_node.resolve(self._client_context) self.assertIsNotNone(result) self.assertEqual("Hello", result) def test_authorise_with_role_as_attrib_access_denied(self): template = ET.fromstring(""" <template> <authorise role="denied"> Hello </authorise> </template> """) ast = self._graph.parse_template_expression(template) self.assertIsNotNone(ast) self.assertIsInstance(ast, TemplateNode) self.assertIsNotNone(ast.children) self.assertEqual(len(ast.children), 1) auth_node = ast.children[0] self.assertIsNotNone(auth_node) self.assertIsInstance(auth_node, TemplateAuthoriseNode) self.assertIsNotNone(auth_node.role) self.assertEqual("denied", auth_node.role) def test_authorise_with_role_as_attrib_and_optional_srai_access_denied(self): template = ET.fromstring(""" <template> <authorise role="denied" denied_srai="NO_ACCESS"> Hello </authorise> </template> """) ast = self._graph.parse_template_expression(template) self.assertIsNotNone(ast) self.assertIsInstance(ast, TemplateNode) self.assertIsNotNone(ast.children) self.assertEqual(len(ast.children), 1) auth_node = ast.children[0] self.assertIsNotNone(auth_node) self.assertIsInstance(auth_node, TemplateAuthoriseNode) self.assertIsNotNone(auth_node.role) self.assertEqual("denied", auth_node.role)

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# importing the neccessary libraries # open cv for image processing import cv2 # used to manipulate different parts import sys # used for manipulating array/matrics import numpy as np # used for accessing the file and folder in the machine import os # used for landmark's facial detector with pre-trained models, the dlib is used to estimate the location of 68 coordinates import dlib from imutils import face_utils # for visulating the image import matplotlib.pyplot as plt # use to retrieve the faces information detector = dlib.get_frontal_face_detector() # print(detector) # function for face detecting and save the Face ROI(embedding) # takes 2 parameter, imagepath = Uploaded image location, name = user name def image_data_generator(imagePath,name): # setting up the path for saving the image path = 'database' # print(path) output -> path # folder for the user to store user image directory = os.path.join(path, name) # print(directory) output -> path/name # Creating the folder for user if the user folder not exist if not os.path.exists(directory): os.makedirs(directory, exist_ok = 'True') # print("\nDirectory with the name {} is created successful".format(name)) # reading the uploaded image image = cv2.imread(imagePath) # print(image) -> print the image value in array [n,n,nc] # plt.imshow(image) -> displaying the image # converting the RGB Image into Gray scale Image gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # print(gray_image) -> print the image value in array [n,n] # plt.imshow(gray_image) # -> displaying the image # detecting the faces in the image, which is similar to detectMultiScale() # faces = face_cascade.detectMultiScale(gray_image) # print(faces) # The 1 in the second argument indicates that we should upsample the image 1 time. This will make everything bigger and allow us to detect more faces. faces = detector(gray_image, 1) #print(faces) # -> print the image value in array [(x,y)(w,h)] # adds a counter to an iterable and returns it in a form of enumerate object for i, d in enumerate(faces): # top, bottom, left, rigth = x, y, w, h # x = left(), y = top() # w = right() - x, h = bottom() - y # roi - region of interest roi_image = gray_image[d.top():d.top() + (d.bottom() - d.top()), d.left():d.left() + (d.right() - d.left())] # saving the roi croped images cv2.imwrite(directory+'/'+name+".jpg",roi_image) imagePath = 'faceDetect.jpg' name = input("\nEnter name of person : ") image_data_generator(imagePath, name) # function for face detecting and save the Face ROI(embedding) from webcam # takes 1 parameter, name = user name def video_data_generator(name): # setting up the path for saving the image path = 'database' # print(path) output -> path # folder for the user to store user image directory = os.path.join(path, name) # print(directory) output -> path/name # Creating the folder for user if the user folder not exist if not os.path.exists(directory): os.makedirs(directory, exist_ok = 'True') # print("\nDirectory with the name {} is created successful".format(name)) # starting up the webcam webcam = cv2.VideoCapture(0) number_of_images = 0 MAX_NUMBER_OF_IMAGES = 20 while number_of_images < MAX_NUMBER_OF_IMAGES: # reading the data from the webcam ret, frame = webcam.read() # flips a 2D array around vertical, horizontal, or both axes # 1 means flipping around y-axis frame = cv2.flip(frame, 1) # converting the rgb frames to gray scale frames # gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # The 1 in the second argument indicates that we should upsample the image 1 time. This will make everything bigger and allow us to detect more faces. faces = detector(frame, 1) #print(faces) # -> print the image value in array [(x,y)(w,h)] # adds a counter to an iterable and returns it in a form of enumerate object for i, d in enumerate(faces): # top, bottom, left, rigth = x, y, w, h # x = left(), y = top() # w = right() - x, h = bottom() - y # roi - region of interest roi_image = frame[d.top():d.top() + (d.bottom() - d.top()), d.left():d.left() + (d.right() - d.left())] # saving the croped image cv2.imwrite(os.path.join(directory, str(name+str(number_of_images)+'.jpg')), roi_image) number_of_images += 1 cv2.rectangle(frame, (d.left(), d.top()), (d.left() + (d.right() - d.left()), d.top() + (d.bottom() - d.top())), (0, 255, 0), 2) # displaying the video cv2.imshow("Webcam",frame) # for closing the stream if(cv2.waitKey(1) & 0xFF == ord('q')): break # stoping the webcam webcam.release() # closing the window cv2.destroyAllWindows() name = input("\nEnter name of person : ") video_data_generator(name)

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import sys from string import ascii_lowercase as alphabets from collections import Counter def main(): inf=100 alphd={x:0 for x in alphabets} ansd={x:inf for x in alphabets} n=int(input()) s=[input() for _ in range(n)] for st in s: for x in st: alphd[x]+=1 for a in alphabets: ansd[a]=min(ansd[a],alphd[a]) alphd[a]=0 print(''.join([a*ansd[a] for a in alphabets if ansd[a]<inf])) def main2(): inf=100 n=int(input()) s=[Counter(input()) for _ in range(n)] ansd={x:inf for x in alphabets} for c in s: for x in alphabets: ansd[x]=min(ansd[x],c[x]) print(''.join([a*ansd[a] for a in alphabets if ansd[a]<inf])) if __name__=='__main__': main2()

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2022-11-19T14:44:55.421926

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# Copyright 2016 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Commands for configuring shared VPC network (XPN) organizations. """ from googlecloudsdk.calliope import base class Organizations(base.Group): """Configure organizations for cross-project networking (XPN)."""

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n = int(input()) a_lst = list(map(int, input().split())) x = a_lst[0] y = sum(a_lst[1:]) diff = abs(y - x) for a in a_lst[1:-1]: x += a y -= a diff = min(diff, abs(y - x)) print(diff)

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############################################################################################################################################################################################################# ############################################################################################################################################################################################################# ### 把 kong_model2 加入 sys.path import os code_exe_path = os.path.realpath(__file__) ### 目前執行 step10_b.py 的 path code_exe_path_element = code_exe_path.split("\\") ### 把 path 切分等等要找出 kong_model 在第幾層 kong_layer = code_exe_path_element.index("kong_model2") ### 找出 kong_model2 在第幾層 kong_model2_dir = "\\".join(code_exe_path_element[:kong_layer + 1]) ### 定位出 kong_model2 的 dir import sys ### 把 kong_model2 加入 sys.path sys.path.append(kong_model2_dir) # print(__file__.split("\\")[-1]) # print(" code_exe_path:", code_exe_path) # print(" code_exe_path_element:", code_exe_path_element) # print(" kong_layer:", kong_layer) # print(" kong_model2_dir:", kong_model2_dir) ############################################################################################################################################################################################################### # 按F5執行時，如果不是在 step10_b.py 的資料夾，自動幫你切過去～才可 import step10_a.py 喔！ code_exe_dir = os.path.dirname(code_exe_path) ### 目前執行 step10_b.py 的 dir if(os.getcwd() != code_exe_dir): ### 如果不是在 step10_b.py 的資料夾，自動幫你切過去～ os.chdir(code_exe_dir) # print("current_path:", os.getcwd()) ############################################################################################################################################################################################################### import Exps_7_v3.doc3d.I_to_M_Gk3_no_pad_BN.pyr_Tcrop256_pad20_jit15.pyr_0s.L5.step10_a as L5_0side import Exps_7_v3.doc3d.I_to_M_Gk3_no_pad_BN.pyr_Tcrop256_pad20_jit15.pyr_1s.L5.step10_a as L5_1side import step10_a as side2 ################################################################################################################################################################################################################################################################################################################################################################################################# ch032_1side_1__2side_all = [ L5_1side.ch032_1side_1, side2.ch032_1side_1__2side_1, ] ch032_1side_2__2side_all = [ L5_1side.ch032_1side_2, side2.ch032_1side_2__2side_1, side2.ch032_1side_2__2side_2, ] ch032_1side_3__2side_all = [ L5_1side.ch032_1side_3, side2.ch032_1side_3__2side_1, side2.ch032_1side_3__2side_2, side2.ch032_1side_3__2side_3, ] ch032_1side_4__2side_all = [ L5_1side.ch032_1side_4, side2.ch032_1side_4__2side_1, side2.ch032_1side_4__2side_2, side2.ch032_1side_4__2side_3, side2.ch032_1side_4__2side_4, ] ch032_1side_5__2side_all = [ L5_1side.ch032_1side_5, side2.ch032_1side_5__2side_1, side2.ch032_1side_5__2side_2, side2.ch032_1side_5__2side_3, side2.ch032_1side_5__2side_4, side2.ch032_1side_5__2side_5, ] ch032_1side_6__2side_all = [ L5_1side.ch032_1side_6, side2.ch032_1side_6__2side_1, side2.ch032_1side_6__2side_2, side2.ch032_1side_6__2side_3, side2.ch032_1side_6__2side_4, side2.ch032_1side_6__2side_5, side2.ch032_1side_6__2side_6, ] ch032_1side_all__2side_all = [ [L5_0side.ch032_0side,], ch032_1side_1__2side_all, ch032_1side_2__2side_all, ch032_1side_3__2side_all, ch032_1side_4__2side_all, ch032_1side_5__2side_all, ch032_1side_6__2side_all, ]

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miseop25/Back_Jun_Code_Study

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2022-11-06T01:05:05.028838

2022-10-23T13:11:22

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def solution(msg): answer = [] lzw_dict = dict() word = "A" for i in range(1, 27) : lzw_dict[word] = i word = chr(ord("A") + i ) m_index = 0 w = msg[0] while m_index < len(msg): if m_index + 1 < len(msg) : temp = w + msg[m_index + 1] else : temp = w if temp in lzw_dict : answer.append(lzw_dict[temp]) else : answer.append(lzw_dict[temp[: -1]]) break if temp in lzw_dict : w = temp m_index += 1 else : i+= 1 lzw_dict[temp] = i answer.append(lzw_dict[temp[: -1]]) m_index += 1 w = msg[m_index] return answer print(solution("KAKAO"))

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richard25@naver.com

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from typing import List import ghidra.pcode.memstate import ghidra.program.model.address import java.lang class UniqueMemoryBank(ghidra.pcode.memstate.MemoryBank): """ An subclass of MemoryBank intended for modeling the "unique" memory space. The space is byte-addressable and paging is not supported. """ class WordInfo(object): initialized: int word: long def __init__(self): ... def equals(self, __a0: object) -> bool: ... def getByte(self, __a0: int) -> int: ... def getClass(self) -> java.lang.Class: ... def getWord(self, __a0: List[int]) -> None: ... def hashCode(self) -> int: ... def notify(self) -> None: ... def notifyAll(self) -> None: ... def setByte(self, __a0: int, __a1: int) -> None: ... def toString(self) -> unicode: ... @overload def wait(self) -> None: ... @overload def wait(self, __a0: long) -> None: ... @overload def wait(self, __a0: long, __a1: int) -> None: ... def __init__(self, spc: ghidra.program.model.address.AddressSpace, isBigEndian: bool): ... def clear(self) -> None: """ Clear unique storage at the start of an instruction """ ... @staticmethod def constructValue(ptr: List[int], offset: int, size: int, bigendian: bool) -> long: ... @staticmethod def deconstructValue(ptr: List[int], offset: int, val: long, size: int, bigendian: bool) -> None: ... def equals(self, __a0: object) -> bool: ... def getChunk(self, offset: long, size: int, dest: List[int], stopOnUninitialized: bool) -> int: ... def getClass(self) -> java.lang.Class: ... def getInitializedMaskSize(self) -> int: """ @return the size of a page initialized mask in bytes. Each bit within the mask corresponds to a data byte within a page. """ ... def getMemoryFaultHandler(self) -> ghidra.pcode.memstate.MemoryFaultHandler: """ @return memory fault handler (may be null) """ ... def getPageSize(self) -> int: """ A MemoryBank is instantiated with a \e natural page size. Requests for large chunks of data may be broken down into units of this size. @return the number of bytes in a page. """ ... def getSpace(self) -> ghidra.program.model.address.AddressSpace: """ @return the AddressSpace associated with this bank. """ ... def hashCode(self) -> int: ... def isBigEndian(self) -> bool: """ @return true if memory bank is big endian """ ... def notify(self) -> None: ... def notifyAll(self) -> None: ... def setChunk(self, offset: long, size: int, src: List[int]) -> None: ... def setInitialized(self, offset: long, size: int, initialized: bool) -> None: ... def toString(self) -> unicode: ... @overload def wait(self) -> None: ... @overload def wait(self, __a0: long) -> None: ... @overload def wait(self, __a0: long, __a1: int) -> None: ...

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tsunekou1019@gmail.com

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"""Proj_DojoNingas URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.11/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.conf.urls import url, include 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls')) """ from django.conf.urls import url, include from django.contrib import admin urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'^', include("apps.dojo_ninjas.urls")), ]

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# .\_schematic.py # -*- coding: utf-8 -*- # PyXB bindings for NM:2b86b09e6504617c4541a8a2f53a65ea784d5722 # Generated 2016-02-15 11:24:52.074000 by PyXB version 1.2.3 # Namespace schematic [xmlns:schematic] import pyxb import pyxb.binding import pyxb.binding.saxer import io import pyxb.utils.utility import pyxb.utils.domutils import sys # Unique identifier for bindings created at the same time _GenerationUID = pyxb.utils.utility.UniqueIdentifier('urn:uuid:057f9670-d409-11e5-9520-7429af7917c0') # Version of PyXB used to generate the bindings _PyXBVersion = '1.2.3' # Generated bindings are not compatible across PyXB versions if pyxb.__version__ != _PyXBVersion: raise pyxb.PyXBVersionError(_PyXBVersion) # Import bindings for namespaces imported into schema import pyxb.binding.datatypes import avm as _ImportedBinding__avm # NOTE: All namespace declarations are reserved within the binding Namespace = pyxb.namespace.NamespaceForURI(u'schematic', create_if_missing=True) Namespace.configureCategories(['typeBinding', 'elementBinding']) def CreateFromDocument (xml_text, default_namespace=None, location_base=None): """Parse the given XML and use the document element to create a Python instance. @param xml_text An XML document. This should be data (Python 2 str or Python 3 bytes), or a text (Python 2 unicode or Python 3 str) in the L{pyxb._InputEncoding} encoding. @keyword default_namespace The L{pyxb.Namespace} instance to use as the default namespace where there is no default namespace in scope. If unspecified or C{None}, the namespace of the module containing this function will be used. @keyword location_base: An object to be recorded as the base of all L{pyxb.utils.utility.Location} instances associated with events and objects handled by the parser. You might pass the URI from which the document was obtained. """ if pyxb.XMLStyle_saxer != pyxb._XMLStyle: dom = pyxb.utils.domutils.StringToDOM(xml_text) return CreateFromDOM(dom.documentElement) if default_namespace is None: default_namespace = Namespace.fallbackNamespace() saxer = pyxb.binding.saxer.make_parser(fallback_namespace=default_namespace, location_base=location_base) handler = saxer.getContentHandler() xmld = xml_text if isinstance(xmld, unicode): xmld = xmld.encode(pyxb._InputEncoding) saxer.parse(io.BytesIO(xmld)) instance = handler.rootObject() return instance def CreateFromDOM (node, default_namespace=None): """Create a Python instance from the given DOM node. The node tag must correspond to an element declaration in this module. @deprecated: Forcing use of DOM interface is unnecessary; use L{CreateFromDocument}.""" if default_namespace is None: default_namespace = Namespace.fallbackNamespace() return pyxb.binding.basis.element.AnyCreateFromDOM(node, default_namespace) # Complex type {schematic}SchematicModel with content type ELEMENT_ONLY class SchematicModel_ (_ImportedBinding__avm.DomainModel_): """Complex type {schematic}SchematicModel with content type ELEMENT_ONLY""" _TypeDefinition = None _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY _Abstract = True _ExpandedName = pyxb.namespace.ExpandedName(Namespace, u'SchematicModel') _XSDLocation = pyxb.utils.utility.Location(u'avm.schematic.xsd', 6, 2) _ElementMap = _ImportedBinding__avm.DomainModel_._ElementMap.copy() _AttributeMap = _ImportedBinding__avm.DomainModel_._AttributeMap.copy() # Base type is _ImportedBinding__avm.DomainModel_ # Element Pin uses Python identifier Pin __Pin = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(None, u'Pin'), 'Pin', '__schematic_SchematicModel__Pin', True, pyxb.utils.utility.Location(u'avm.schematic.xsd', 10, 10), ) Pin = property(__Pin.value, __Pin.set, None, None) # Attribute UsesResource inherited from {avm}DomainModel # Attribute Author inherited from {avm}DomainModel # Attribute Notes inherited from {avm}DomainModel # Attribute XPosition inherited from {avm}DomainModel # Attribute YPosition inherited from {avm}DomainModel # Attribute Name inherited from {avm}DomainModel # Attribute ID inherited from {avm}DomainModel _ElementMap.update({ __Pin.name() : __Pin }) _AttributeMap.update({ }) Namespace.addCategoryObject('typeBinding', u'SchematicModel', SchematicModel_) # Complex type {schematic}Pin with content type EMPTY class Pin_ (_ImportedBinding__avm.DomainModelPort_): """Complex type {schematic}Pin with content type EMPTY""" _TypeDefinition = None _ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_EMPTY _Abstract = False _ExpandedName = pyxb.namespace.ExpandedName(Namespace, u'Pin') _XSDLocation = pyxb.utils.utility.Location(u'avm.schematic.xsd', 15, 2) _ElementMap = _ImportedBinding__avm.DomainModelPort_._ElementMap.copy() _AttributeMap = _ImportedBinding__avm.DomainModelPort_._AttributeMap.copy() # Base type is _ImportedBinding__avm.DomainModelPort_ # Attribute Notes inherited from {avm}Port # Attribute XPosition inherited from {avm}Port # Attribute Definition inherited from {avm}Port # Attribute YPosition inherited from {avm}Port # Attribute Name inherited from {avm}Port # Attribute ID inherited from {avm}PortMapTarget # Attribute PortMap inherited from {avm}PortMapTarget # Attribute EDAGate uses Python identifier EDAGate __EDAGate = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(None, u'EDAGate'), 'EDAGate', '__schematic_Pin__EDAGate', pyxb.binding.datatypes.string) __EDAGate._DeclarationLocation = pyxb.utils.utility.Location(u'avm.schematic.xsd', 18, 8) __EDAGate._UseLocation = pyxb.utils.utility.Location(u'avm.schematic.xsd', 18, 8) EDAGate = property(__EDAGate.value, __EDAGate.set, None, None) # Attribute EDASymbolLocationX uses Python identifier EDASymbolLocationX __EDASymbolLocationX = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(None, u'EDASymbolLocationX'), 'EDASymbolLocationX', '__schematic_Pin__EDASymbolLocationX', pyxb.binding.datatypes.string) __EDASymbolLocationX._DeclarationLocation = pyxb.utils.utility.Location(u'avm.schematic.xsd', 19, 8) __EDASymbolLocationX._UseLocation = pyxb.utils.utility.Location(u'avm.schematic.xsd', 19, 8) EDASymbolLocationX = property(__EDASymbolLocationX.value, __EDASymbolLocationX.set, None, None) # Attribute EDASymbolLocationY uses Python identifier EDASymbolLocationY __EDASymbolLocationY = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(None, u'EDASymbolLocationY'), 'EDASymbolLocationY', '__schematic_Pin__EDASymbolLocationY', pyxb.binding.datatypes.string) __EDASymbolLocationY._DeclarationLocation = pyxb.utils.utility.Location(u'avm.schematic.xsd', 20, 8) __EDASymbolLocationY._UseLocation = pyxb.utils.utility.Location(u'avm.schematic.xsd', 20, 8) EDASymbolLocationY = property(__EDASymbolLocationY.value, __EDASymbolLocationY.set, None, None) # Attribute EDASymbolRotation uses Python identifier EDASymbolRotation __EDASymbolRotation = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(None, u'EDASymbolRotation'), 'EDASymbolRotation', '__schematic_Pin__EDASymbolRotation', pyxb.binding.datatypes.string) __EDASymbolRotation._DeclarationLocation = pyxb.utils.utility.Location(u'avm.schematic.xsd', 21, 8) __EDASymbolRotation._UseLocation = pyxb.utils.utility.Location(u'avm.schematic.xsd', 21, 8) EDASymbolRotation = property(__EDASymbolRotation.value, __EDASymbolRotation.set, None, None) # Attribute SPICEPortNumber uses Python identifier SPICEPortNumber __SPICEPortNumber = pyxb.binding.content.AttributeUse(pyxb.namespace.ExpandedName(None, u'SPICEPortNumber'), 'SPICEPortNumber', '__schematic_Pin__SPICEPortNumber', pyxb.binding.datatypes.unsignedInt) __SPICEPortNumber._DeclarationLocation = pyxb.utils.utility.Location(u'avm.schematic.xsd', 22, 8) __SPICEPortNumber._UseLocation = pyxb.utils.utility.Location(u'avm.schematic.xsd', 22, 8) SPICEPortNumber = property(__SPICEPortNumber.value, __SPICEPortNumber.set, None, None) _ElementMap.update({ }) _AttributeMap.update({ __EDAGate.name() : __EDAGate, __EDASymbolLocationX.name() : __EDASymbolLocationX, __EDASymbolLocationY.name() : __EDASymbolLocationY, __EDASymbolRotation.name() : __EDASymbolRotation, __SPICEPortNumber.name() : __SPICEPortNumber }) Namespace.addCategoryObject('typeBinding', u'Pin', Pin_) SchematicModel = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, u'SchematicModel'), SchematicModel_, location=pyxb.utils.utility.Location(u'avm.schematic.xsd', 4, 2)) Namespace.addCategoryObject('elementBinding', SchematicModel.name().localName(), SchematicModel) Pin = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, u'Pin'), Pin_, location=pyxb.utils.utility.Location(u'avm.schematic.xsd', 5, 2)) Namespace.addCategoryObject('elementBinding', Pin.name().localName(), Pin) SchematicModel_._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(None, u'Pin'), Pin_, scope=SchematicModel_, location=pyxb.utils.utility.Location(u'avm.schematic.xsd', 10, 10))) def _BuildAutomaton (): # Remove this helper function from the namespace after it is invoked global _BuildAutomaton del _BuildAutomaton import pyxb.utils.fac as fac counters = set() cc_0 = fac.CounterCondition(min=0L, max=None, metadata=pyxb.utils.utility.Location(u'avm.schematic.xsd', 10, 10)) counters.add(cc_0) states = [] final_update = set() final_update.add(fac.UpdateInstruction(cc_0, False)) symbol = pyxb.binding.content.ElementUse(SchematicModel_._UseForTag(pyxb.namespace.ExpandedName(None, u'Pin')), pyxb.utils.utility.Location(u'avm.schematic.xsd', 10, 10)) st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False) states.append(st_0) transitions = [] transitions.append(fac.Transition(st_0, [ fac.UpdateInstruction(cc_0, True) ])) st_0._set_transitionSet(transitions) return fac.Automaton(states, counters, True, containing_state=None) SchematicModel_._Automaton = _BuildAutomaton()

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# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from . import outputs __all__ = [ 'GetVirtualNetworkGatewayBgpPeerStatusResult', 'AwaitableGetVirtualNetworkGatewayBgpPeerStatusResult', 'get_virtual_network_gateway_bgp_peer_status', ] @pulumi.output_type class GetVirtualNetworkGatewayBgpPeerStatusResult: """ Response for list BGP peer status API service call """ def __init__(__self__, value=None): if value and not isinstance(value, list): raise TypeError("Expected argument 'value' to be a list") pulumi.set(__self__, "value", value) @property @pulumi.getter def value(self) -> Optional[Sequence['outputs.BgpPeerStatusResponse']]: """ List of BGP peers """ return pulumi.get(self, "value") class AwaitableGetVirtualNetworkGatewayBgpPeerStatusResult(GetVirtualNetworkGatewayBgpPeerStatusResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetVirtualNetworkGatewayBgpPeerStatusResult( value=self.value) def get_virtual_network_gateway_bgp_peer_status(peer: Optional[str] = None, resource_group_name: Optional[str] = None, virtual_network_gateway_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetVirtualNetworkGatewayBgpPeerStatusResult: """ Response for list BGP peer status API service call :param str peer: The IP address of the peer to retrieve the status of. :param str resource_group_name: The name of the resource group. :param str virtual_network_gateway_name: The name of the virtual network gateway. """ __args__ = dict() __args__['peer'] = peer __args__['resourceGroupName'] = resource_group_name __args__['virtualNetworkGatewayName'] = virtual_network_gateway_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:network/v20180201:getVirtualNetworkGatewayBgpPeerStatus', __args__, opts=opts, typ=GetVirtualNetworkGatewayBgpPeerStatusResult).value return AwaitableGetVirtualNetworkGatewayBgpPeerStatusResult( value=__ret__.value)

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# ext/mutable.py # Copyright (C) 2005-2013 the SQLAlchemy authors and contributors <see AUTHORS file> # # This module is part of SQLAlchemy and is released under # the MIT License: http://www.opensource.org/licenses/mit-license.php """Provide support for tracking of in-place changes to scalar values, which are propagated into ORM change events on owning parent objects. The :mod:`sqlalchemy.ext.mutable` extension replaces SQLAlchemy's legacy approach to in-place mutations of scalar values, established by the :class:`.types.MutableType` class as well as the ``mutable=True`` type flag, with a system that allows change events to be propagated from the value to the owning parent, thereby removing the need for the ORM to maintain copies of values as well as the very expensive requirement of scanning through all "mutable" values on each flush call, looking for changes. .. _mutable_scalars: Establishing Mutability on Scalar Column Values =============================================== A typical example of a "mutable" structure is a Python dictionary. Following the example introduced in :ref:`types_toplevel`, we begin with a custom type that marshals Python dictionaries into JSON strings before being persisted:: from sqlalchemy.types import TypeDecorator, VARCHAR import json class JSONEncodedDict(TypeDecorator): "Represents an immutable structure as a json-encoded string." impl = VARCHAR def process_bind_param(self, value, dialect): if value is not None: value = json.dumps(value) return value def process_result_value(self, value, dialect): if value is not None: value = json.loads(value) return value The usage of ``json`` is only for the purposes of example. The :mod:`sqlalchemy.ext.mutable` extension can be used with any type whose target Python type may be mutable, including :class:`.PickleType`, :class:`.postgresql.ARRAY`, etc. When using the :mod:`sqlalchemy.ext.mutable` extension, the value itself tracks all parents which reference it. Below, we illustrate the a simple version of the :class:`.MutableDict` dictionary object, which applies the :class:`.Mutable` mixin to a plain Python dictionary:: import collections from sqlalchemy.ext.mutable import Mutable class MutableDict(Mutable, dict): @classmethod def coerce(cls, key, value): "Convert plain dictionaries to MutableDict." if not isinstance(value, MutableDict): if isinstance(value, dict): return MutableDict(value) # this call will raise ValueError return Mutable.coerce(key, value) else: return value def __setitem__(self, key, value): "Detect dictionary set events and emit change events." dict.__setitem__(self, key, value) self.changed() def __delitem__(self, key): "Detect dictionary del events and emit change events." dict.__delitem__(self, key) self.changed() The above dictionary class takes the approach of subclassing the Python built-in ``dict`` to produce a dict subclass which routes all mutation events through ``__setitem__``. There are variants on this approach, such as subclassing ``UserDict.UserDict`` or ``collections.MutableMapping``; the part that's important to this example is that the :meth:`.Mutable.changed` method is called whenever an in-place change to the datastructure takes place. We also redefine the :meth:`.Mutable.coerce` method which will be used to convert any values that are not instances of ``MutableDict``, such as the plain dictionaries returned by the ``json`` module, into the appropriate type. Defining this method is optional; we could just as well created our ``JSONEncodedDict`` such that it always returns an instance of ``MutableDict``, and additionally ensured that all calling code uses ``MutableDict`` explicitly. When :meth:`.Mutable.coerce` is not overridden, any values applied to a parent object which are not instances of the mutable type will raise a ``ValueError``. Our new ``MutableDict`` type offers a class method :meth:`~.Mutable.as_mutable` which we can use within column metadata to associate with types. This method grabs the given type object or class and associates a listener that will detect all future mappings of this type, applying event listening instrumentation to the mapped attribute. Such as, with classical table metadata:: from sqlalchemy import Table, Column, Integer my_data = Table('my_data', metadata, Column('id', Integer, primary_key=True), Column('data', MutableDict.as_mutable(JSONEncodedDict)) ) Above, :meth:`~.Mutable.as_mutable` returns an instance of ``JSONEncodedDict`` (if the type object was not an instance already), which will intercept any attributes which are mapped against this type. Below we establish a simple mapping against the ``my_data`` table:: from sqlalchemy import mapper class MyDataClass(object): pass # associates mutation listeners with MyDataClass.data mapper(MyDataClass, my_data) The ``MyDataClass.data`` member will now be notified of in place changes to its value. There's no difference in usage when using declarative:: from sqlalchemy.ext.declarative import declarative_base Base = declarative_base() class MyDataClass(Base): __tablename__ = 'my_data' id = Column(Integer, primary_key=True) data = Column(MutableDict.as_mutable(JSONEncodedDict)) Any in-place changes to the ``MyDataClass.data`` member will flag the attribute as "dirty" on the parent object:: >>> from sqlalchemy.orm import Session >>> sess = Session() >>> m1 = MyDataClass(data={'value1':'foo'}) >>> sess.add(m1) >>> sess.commit() >>> m1.data['value1'] = 'bar' >>> assert m1 in sess.dirty True The ``MutableDict`` can be associated with all future instances of ``JSONEncodedDict`` in one step, using :meth:`~.Mutable.associate_with`. This is similar to :meth:`~.Mutable.as_mutable` except it will intercept all occurrences of ``MutableDict`` in all mappings unconditionally, without the need to declare it individually:: MutableDict.associate_with(JSONEncodedDict) class MyDataClass(Base): __tablename__ = 'my_data' id = Column(Integer, primary_key=True) data = Column(JSONEncodedDict) Supporting Pickling -------------------- The key to the :mod:`sqlalchemy.ext.mutable` extension relies upon the placement of a ``weakref.WeakKeyDictionary`` upon the value object, which stores a mapping of parent mapped objects keyed to the attribute name under which they are associated with this value. ``WeakKeyDictionary`` objects are not picklable, due to the fact that they contain weakrefs and function callbacks. In our case, this is a good thing, since if this dictionary were picklable, it could lead to an excessively large pickle size for our value objects that are pickled by themselves outside of the context of the parent. The developer responsibility here is only to provide a ``__getstate__`` method that excludes the :meth:`~.MutableBase._parents` collection from the pickle stream:: class MyMutableType(Mutable): def __getstate__(self): d = self.__dict__.copy() d.pop('_parents', None) return d With our dictionary example, we need to return the contents of the dict itself (and also restore them on __setstate__):: class MutableDict(Mutable, dict): # .... def __getstate__(self): return dict(self) def __setstate__(self, state): self.update(state) In the case that our mutable value object is pickled as it is attached to one or more parent objects that are also part of the pickle, the :class:`.Mutable` mixin will re-establish the :attr:`.Mutable._parents` collection on each value object as the owning parents themselves are unpickled. .. _mutable_composites: Establishing Mutability on Composites ===================================== Composites are a special ORM feature which allow a single scalar attribute to be assigned an object value which represents information "composed" from one or more columns from the underlying mapped table. The usual example is that of a geometric "point", and is introduced in :ref:`mapper_composite`. .. versionchanged:: 0.7 The internals of :func:`.orm.composite` have been greatly simplified and in-place mutation detection is no longer enabled by default; instead, the user-defined value must detect changes on its own and propagate them to all owning parents. The :mod:`sqlalchemy.ext.mutable` extension provides the helper class :class:`.MutableComposite`, which is a slight variant on the :class:`.Mutable` class. As is the case with :class:`.Mutable`, the user-defined composite class subclasses :class:`.MutableComposite` as a mixin, and detects and delivers change events to its parents via the :meth:`.MutableComposite.changed` method. In the case of a composite class, the detection is usually via the usage of Python descriptors (i.e. ``@property``), or alternatively via the special Python method ``__setattr__()``. Below we expand upon the ``Point`` class introduced in :ref:`mapper_composite` to subclass :class:`.MutableComposite` and to also route attribute set events via ``__setattr__`` to the :meth:`.MutableComposite.changed` method:: from sqlalchemy.ext.mutable import MutableComposite class Point(MutableComposite): def __init__(self, x, y): self.x = x self.y = y def __setattr__(self, key, value): "Intercept set events" # set the attribute object.__setattr__(self, key, value) # alert all parents to the change self.changed() def __composite_values__(self): return self.x, self.y def __eq__(self, other): return isinstance(other, Point) and \\ other.x == self.x and \\ other.y == self.y def __ne__(self, other): return not self.__eq__(other) The :class:`.MutableComposite` class uses a Python metaclass to automatically establish listeners for any usage of :func:`.orm.composite` that specifies our ``Point`` type. Below, when ``Point`` is mapped to the ``Vertex`` class, listeners are established which will route change events from ``Point`` objects to each of the ``Vertex.start`` and ``Vertex.end`` attributes:: from sqlalchemy.orm import composite, mapper from sqlalchemy import Table, Column vertices = Table('vertices', metadata, Column('id', Integer, primary_key=True), Column('x1', Integer), Column('y1', Integer), Column('x2', Integer), Column('y2', Integer), ) class Vertex(object): pass mapper(Vertex, vertices, properties={ 'start': composite(Point, vertices.c.x1, vertices.c.y1), 'end': composite(Point, vertices.c.x2, vertices.c.y2) }) Any in-place changes to the ``Vertex.start`` or ``Vertex.end`` members will flag the attribute as "dirty" on the parent object:: >>> from sqlalchemy.orm import Session >>> sess = Session() >>> v1 = Vertex(start=Point(3, 4), end=Point(12, 15)) >>> sess.add(v1) >>> sess.commit() >>> v1.end.x = 8 >>> assert v1 in sess.dirty True Coercing Mutable Composites --------------------------- The :meth:`.MutableBase.coerce` method is also supported on composite types. In the case of :class:`.MutableComposite`, the :meth:`.MutableBase.coerce` method is only called for attribute set operations, not load operations. Overriding the :meth:`.MutableBase.coerce` method is essentially equivalent to using a :func:`.validates` validation routine for all attributes which make use of the custom composite type:: class Point(MutableComposite): # other Point methods # ... def coerce(cls, key, value): if isinstance(value, tuple): value = Point(*value) elif not isinstance(value, Point): raise ValueError("tuple or Point expected") return value .. versionadded:: 0.7.10,0.8.0b2 Support for the :meth:`.MutableBase.coerce` method in conjunction with objects of type :class:`.MutableComposite`. Supporting Pickling -------------------- As is the case with :class:`.Mutable`, the :class:`.MutableComposite` helper class uses a ``weakref.WeakKeyDictionary`` available via the :meth:`.MutableBase._parents` attribute which isn't picklable. If we need to pickle instances of ``Point`` or its owning class ``Vertex``, we at least need to define a ``__getstate__`` that doesn't include the ``_parents`` dictionary. Below we define both a ``__getstate__`` and a ``__setstate__`` that package up the minimal form of our ``Point`` class:: class Point(MutableComposite): # ... def __getstate__(self): return self.x, self.y def __setstate__(self, state): self.x, self.y = state As with :class:`.Mutable`, the :class:`.MutableComposite` augments the pickling process of the parent's object-relational state so that the :meth:`.MutableBase._parents` collection is restored to all ``Point`` objects. """ from ..orm.attributes import flag_modified from .. import event, types from ..orm import mapper, object_mapper, Mapper from ..util import memoized_property import weakref class MutableBase(object): """Common base class to :class:`.Mutable` and :class:`.MutableComposite`. """ @memoized_property def _parents(self): """Dictionary of parent object->attribute name on the parent. This attribute is a so-called "memoized" property. It initializes itself with a new ``weakref.WeakKeyDictionary`` the first time it is accessed, returning the same object upon subsequent access. """ return weakref.WeakKeyDictionary() @classmethod def coerce(cls, key, value): """Given a value, coerce it into the target type. Can be overridden by custom subclasses to coerce incoming data into a particular type. By default, raises ``ValueError``. This method is called in different scenarios depending on if the parent class is of type :class:`.Mutable` or of type :class:`.MutableComposite`. In the case of the former, it is called for both attribute-set operations as well as during ORM loading operations. For the latter, it is only called during attribute-set operations; the mechanics of the :func:`.composite` construct handle coercion during load operations. :param key: string name of the ORM-mapped attribute being set. :param value: the incoming value. :return: the method should return the coerced value, or raise ``ValueError`` if the coercion cannot be completed. """ if value is None: return None msg = "Attribute '%s' does not accept objects of type %s" raise ValueError(msg % (key, type(value))) @classmethod def _listen_on_attribute(cls, attribute, coerce, parent_cls): """Establish this type as a mutation listener for the given mapped descriptor. """ key = attribute.key if parent_cls is not attribute.class_: return # rely on "propagate" here parent_cls = attribute.class_ def load(state, *args): """Listen for objects loaded or refreshed. Wrap the target data member's value with ``Mutable``. """ val = state.dict.get(key, None) if val is not None: if coerce: val = cls.coerce(key, val) state.dict[key] = val val._parents[state.obj()] = key def set(target, value, oldvalue, initiator): """Listen for set/replace events on the target data member. Establish a weak reference to the parent object on the incoming value, remove it for the one outgoing. """ if not isinstance(value, cls): value = cls.coerce(key, value) if value is not None: value._parents[target.obj()] = key if isinstance(oldvalue, cls): oldvalue._parents.pop(target.obj(), None) return value def pickle(state, state_dict): val = state.dict.get(key, None) if val is not None: if 'ext.mutable.values' not in state_dict: state_dict['ext.mutable.values'] = [] state_dict['ext.mutable.values'].append(val) def unpickle(state, state_dict): if 'ext.mutable.values' in state_dict: for val in state_dict['ext.mutable.values']: val._parents[state.obj()] = key event.listen(parent_cls, 'load', load, raw=True, propagate=True) event.listen(parent_cls, 'refresh', load, raw=True, propagate=True) event.listen(attribute, 'set', set, raw=True, retval=True, propagate=True) event.listen(parent_cls, 'pickle', pickle, raw=True, propagate=True) event.listen(parent_cls, 'unpickle', unpickle, raw=True, propagate=True) class Mutable(MutableBase): """Mixin that defines transparent propagation of change events to a parent object. See the example in :ref:`mutable_scalars` for usage information. """ def changed(self): """Subclasses should call this method whenever change events occur.""" for parent, key in self._parents.items(): flag_modified(parent, key) @classmethod def associate_with_attribute(cls, attribute): """Establish this type as a mutation listener for the given mapped descriptor. """ cls._listen_on_attribute(attribute, True, attribute.class_) @classmethod def associate_with(cls, sqltype): """Associate this wrapper with all future mapped columns of the given type. This is a convenience method that calls ``associate_with_attribute`` automatically. .. warning:: The listeners established by this method are *global* to all mappers, and are *not* garbage collected. Only use :meth:`.associate_with` for types that are permanent to an application, not with ad-hoc types else this will cause unbounded growth in memory usage. """ def listen_for_type(mapper, class_): for prop in mapper.column_attrs: if isinstance(prop.columns[0].type, sqltype): cls.associate_with_attribute(getattr(class_, prop.key)) event.listen(mapper, 'mapper_configured', listen_for_type) @classmethod def as_mutable(cls, sqltype): """Associate a SQL type with this mutable Python type. This establishes listeners that will detect ORM mappings against the given type, adding mutation event trackers to those mappings. The type is returned, unconditionally as an instance, so that :meth:`.as_mutable` can be used inline:: Table('mytable', metadata, Column('id', Integer, primary_key=True), Column('data', MyMutableType.as_mutable(PickleType)) ) Note that the returned type is always an instance, even if a class is given, and that only columns which are declared specifically with that type instance receive additional instrumentation. To associate a particular mutable type with all occurrences of a particular type, use the :meth:`.Mutable.associate_with` classmethod of the particular :meth:`.Mutable` subclass to establish a global association. .. warning:: The listeners established by this method are *global* to all mappers, and are *not* garbage collected. Only use :meth:`.as_mutable` for types that are permanent to an application, not with ad-hoc types else this will cause unbounded growth in memory usage. """ sqltype = types.to_instance(sqltype) def listen_for_type(mapper, class_): for prop in mapper.column_attrs: if prop.columns[0].type is sqltype: cls.associate_with_attribute(getattr(class_, prop.key)) event.listen(mapper, 'mapper_configured', listen_for_type) return sqltype class MutableComposite(MutableBase): """Mixin that defines transparent propagation of change events on a SQLAlchemy "composite" object to its owning parent or parents. See the example in :ref:`mutable_composites` for usage information. """ def changed(self): """Subclasses should call this method whenever change events occur.""" for parent, key in self._parents.items(): prop = object_mapper(parent).get_property(key) for value, attr_name in zip( self.__composite_values__(), prop._attribute_keys): setattr(parent, attr_name, value) def _setup_composite_listener(): def _listen_for_type(mapper, class_): for prop in mapper.iterate_properties: if (hasattr(prop, 'composite_class') and isinstance(prop.composite_class, type) and issubclass(prop.composite_class, MutableComposite)): prop.composite_class._listen_on_attribute( getattr(class_, prop.key), False, class_) if not Mapper.dispatch.mapper_configured._contains(Mapper, _listen_for_type): event.listen(Mapper, 'mapper_configured', _listen_for_type) _setup_composite_listener() class MutableDict(Mutable, dict): """A dictionary type that implements :class:`.Mutable`. .. versionadded:: 0.8 """ def __setitem__(self, key, value): """Detect dictionary set events and emit change events.""" dict.__setitem__(self, key, value) self.changed() def __delitem__(self, key): """Detect dictionary del events and emit change events.""" dict.__delitem__(self, key) self.changed() def clear(self): dict.clear(self) self.changed() @classmethod def coerce(cls, key, value): """Convert plain dictionary to MutableDict.""" if not isinstance(value, MutableDict): if isinstance(value, dict): return MutableDict(value) return Mutable.coerce(key, value) else: return value def __getstate__(self): return dict(self) def __setstate__(self, state): self.update(state)

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# Event: LCCS Python Fundamental Skills Workshop # Date: May 2018 # Author: Joe English, PDST # eMail: computerscience@pdst.ie # Purpose: Turtle Graphics - Further Activities # Match the code blocks below to the corresponding shape from turtle import * # import the turtle graphics library forward(100) right(90) forward(50) right(90) forward(100) right(90) forward(50)

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from django.urls import path from . import views ''' app_name = 'polls' urlpatterns = [ # ex: /polls/ path('', views.index, name='index'), # ex: /polls/5/ path('<int:question_id>/', views.detail, name='detail'), # ex: /polls/5/results/ path('<int:question_id>/results/', views.results, name='results'), # ex: /polls/5/vote/ path('<int:question_id>/vote/', views.vote, name='vote'), ] ''' app_name = 'polls' urlpatterns = [ path('', views.IndexView.as_view(), name='index'), path('<int:pk>/', views.DetailView.as_view(), name='detail'), path('<int:pk>/results/', views.ResultsView.as_view(), name='results'), path('<int:question_id>/vote/', views.vote, name='vote'), ]

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# -*- coding: utf-8 -*- # Generated by Django 1.11.2 on 2017-08-28 16:27 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('studies', '0030_merge_20170827_1909'), ('studies', '0030_merge_20170827_1539'), ] operations = [ ]

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# 二叉搜索树,取个英文名字方便调包 class BiTreeNode: def __init__(self, data): self.data = data self.lchild = None # 左孩子节点 self.rchild = None # 右孩子节点 self.parent = None class BST: def __init__(self, li=None): self.root = None if li: for val in li: self.insert_no_rec(val) ############################################## 插入功能 ################################################ def insert(self, node, val): if not node: # 当前节点为None,就改变这个位置的值 node = BiTreeNode(val) elif node.data > val: # 如果值改变了那就与左孩子建立联系,如果没改变就当说了句废话 node.lchild = self.insert(node.lchild, val) # 如果node.lchild有值就接着比,没有就落户了 node.lchild.parent = node elif node.data < val: # 不考虑插入相同元素的情况 node.rchild = self.insert(node.rchild, val) node.rchild.parent = node return node def insert_no_rec(self, val): # 非递归形式的插入 p = self.root if not p: # 空树 self.root = BiTreeNode(val) return while 1: if p.data > val: if p.lchild: # 存在左孩子 p = p.lchild else: # 左边没有节点,捏一个节点 p.lchild = BiTreeNode(val) p.lchild.parent = p return elif p.data < val: if p.rchild: p = p.rchild else: p.rchild = BiTreeNode(val) p.rchild.parent = p return ############################################## 插入功能 ################################################ ############################################## 查询功能 ################################################ def query(self, node, val): # 查询功能,递归版本 if not node: return None if node.data < val: return self.query(node.rchild, val) elif node.data > val: return self.query(node.lchild, val) else: return node def query_no_rec(self, val): p = self.root while p: if p.data > val: p = p.lchild elif p.data < val: p = p.rchild else: return p ############################################## 查询功能 ################################################ ###################################### 遍历打印功能 ####################################### def pre_order(self, root): # 前序遍历树的节点,使用递归实现 if root: print(root.data, end=',') self.pre_order(root.lchild) self.pre_order(root.rchild) def in_order(self, root): if root: self.in_order(root.lchild) print(root.data, end=',') self.in_order(root.rchild) def post_order(self, root): if root: self.post_order(root.lchild) self.post_order(root.rchild) print(root.data, end=',') ###################################### 遍历打印功能 ####################################### ###################################### 删除功能 ####################################### def __remove_node_1(self, node): # 情况1: 删除的节点是叶子节点,两个下划线表示类内方法 if not node.parent: # node是根节点 self.root = None elif node == node.parent.lchild: # node是它父节点的左孩子 node.parent.lchild = None else: # node是它父节点的右孩子 node.parent.rchild = None def __remove_node_21(self, node): # 情况2.1: 删除的节点不是叶子节点,且其只有左孩子 if not node.parent: # node是根节点 self.root = node.lchild node.lchild.parent = None elif node == node.parent.lchild: # node是其父节点的左孩子节点 node.parent.lchild = node.lchild node.lchild.parent = node.parent else: # node是其父节点的右孩子节点 node.parent.rchild = node.rchild node.rchild.parent = node.parent def __remove_node_22(self, node): # 情况2.2: 删除的节点非叶子节点,且其只有右孩子 if not node.parent: self.root = node.rchild node.rchild.parent = None elif node == node.parent.lchild: # node是其父节点的左孩子节点 node.parent.lchild = node.rchild node.rchild.parent = node.parent else: # node是其父节点的右孩子节点 node.parent.rchild = node.rchild node.rchild.parent = node.parent def delete(self, val): if self.root: # 不是空树 node = self.query_no_rec(val) if not node: return False # 没找到要删除的节点 if not node.lchild and not node.rchild: # 情况1:叶子节点 self.__remove_node_1(node) elif not node.rchild: # 情况2.1:只有左孩子节点 self.__remove_node_21(node) elif not node.lchild: # 情况2.2:只有右孩子节点 self.__remove_node_22(node) else: # 情况3:有两个节点,找右孩子的最小节点 min_node = node.rchild while min_node.lchild: min_node = min_node.lchild node.data = min_node.data if min_node.rchild: self.__remove_node_22(min_node) else: self.__remove_node_1(min_node) ###################################### 删除功能 ####################################### # tree = BST([4,6,7,9,2,1,3,5,8]) # tree.pre_order(tree.root) # print('') # tree.in_order(tree.root) # 升序的 # print('\n', tree.query_no_rec(4).data) # print(tree.query_no_rec(11)) # # tree.delete(4) # tree.delete(1) # tree.delete(8) # tree.in_order(tree.root)

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#!/usr/bin/env python # File created on 09 Feb 2010 from __future__ import division __author__ = "Greg Caporaso" __copyright__ = "Copyright 2011, The QIIME Project" __credits__ = ["Greg Caporaso"] __license__ = "GPL" __version__ = "1.5.0-dev" __maintainer__ = "Greg Caporaso" __email__ = "gregcaporaso@gmail.com" __status__ = "Development" import warnings warnings.filterwarnings('ignore', 'Not using MPI as mpi4py not found') from qiime.util import (parse_command_line_parameters, get_options_lookup, make_option, load_qiime_config) from qiime.align_seqs import pairwise_alignment_methods from qiime.parallel.align_seqs import ParallelAlignSeqsPyNast qiime_config = load_qiime_config() options_lookup = get_options_lookup() script_info={} script_info['brief_description']="""Parallel sequence alignment using PyNAST""" script_info['script_description']="""A wrapper for the align_seqs.py PyNAST option, intended to make use of multicore/multiprocessor environments to perform analyses in parallel.""" script_info['script_usage']=[] script_info['script_usage'].append(("""Example""","""Align the input file (-i) against using PyNAST and write the output (-o) to $PWD/pynast_aligned_seqs/. ALWAYS SPECIFY ABSOLUTE FILE PATHS (absolute path represented here as $PWD, but will generally look something like /home/ubuntu/my_analysis/).""","""%prog -i $PWD/inseqs.fasta -o $PWD/pynast_aligned_seqs/""")) script_info['output_description']="""This results in a multiple sequence alignment (FASTA-formatted).""" script_info['required_options'] = [\ options_lookup['fasta_as_primary_input'],\ options_lookup['output_dir'] ] pairwise_alignment_method_choices = pairwise_alignment_methods.keys() blast_db_default_help =\ qiime_config['pynast_template_alignment_blastdb'] or \ 'created on-the-fly from template_alignment' script_info['optional_options'] = [\ make_option('-a','--pairwise_alignment_method',\ type='choice',help='Method to use for pairwise alignments'+\ ' [default: %default]',\ default='uclust',choices=pairwise_alignment_method_choices),\ make_option('-d','--blast_db',\ dest='blast_db',help='Database to blast against'+\ ' [default: %s]' % blast_db_default_help, default=qiime_config['pynast_template_alignment_blastdb']),\ make_option('-e','--min_length',\ type='int',help='Minimum sequence '+\ 'length to include in alignment [default: 75% of the'+\ ' median input sequence length]',\ default=-1), make_option('-p','--min_percent_id',action='store',\ type='float',help='Minimum percent '+\ 'sequence identity to closest blast hit to include sequence in'+\ ' alignment [default: %default]',default=75.0),\ options_lookup['jobs_to_start'], options_lookup['retain_temp_files'], options_lookup['suppress_submit_jobs'], options_lookup['poll_directly'], options_lookup['cluster_jobs_fp'], options_lookup['suppress_polling'], options_lookup['job_prefix'], options_lookup['seconds_to_sleep'] ] script_info['version'] = __version__ # pynast_template_alignment_fp is required only if it is not # provided in qiime_config if qiime_config['pynast_template_alignment_fp']: script_info['optional_options'].append(make_option('-t','--template_fp',\ type='string',dest='template_fp',help='Filepath for '+\ 'template against [default: %default]', default=qiime_config['pynast_template_alignment_fp'])) else: script_info['required_options'].append(make_option('-t','--template_fp',\ type='string',dest='template_fp',\ help='Filepath for template against', default=qiime_config['pynast_template_alignment_fp'])) def main(): option_parser, opts, args = parse_command_line_parameters(**script_info) # create dict of command-line options params = eval(str(opts)) parallel_runner = ParallelAlignSeqsPyNast( cluster_jobs_fp=opts.cluster_jobs_fp, jobs_to_start=opts.jobs_to_start, retain_temp_files=opts.retain_temp_files, suppress_polling=opts.suppress_polling, seconds_to_sleep=opts.seconds_to_sleep) parallel_runner(opts.input_fasta_fp, opts.output_dir, params, job_prefix=opts.job_prefix, poll_directly=opts.poll_directly, suppress_submit_jobs=False) if __name__ == "__main__": main()

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""" BIMData API BIMData API is a tool to interact with your models stored on BIMData’s servers. Through the API, you can manage your projects, the clouds, upload your IFC files and manage them through endpoints. # noqa: E501 The version of the OpenAPI document: v1 (v1) Contact: support@bimdata.io Generated by: https://openapi-generator.tech """ import sys import unittest import bimdata_api_client from bimdata_api_client.model.raw_material import RawMaterial class TestRawMaterial(unittest.TestCase): """RawMaterial unit test stubs""" def setUp(self): pass def tearDown(self): pass def testRawMaterial(self): """Test RawMaterial""" # FIXME: construct object with mandatory attributes with example values # model = RawMaterial() # noqa: E501 pass if __name__ == '__main__': unittest.main()

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from __future__ import print_function import PILasOPENCV as Image import PILasOPENCV as ImageDraw import PILasOPENCV as ImageFont import cv2 # font = ImageFont.truetype("arial.ttf", 30) size = 20 font = ImageFont.truetype("msgothic.ttc", 22+int(size/50), index=0, encoding="unic") print(font) im = Image.new("RGB", (512, 512), "grey") draw = ImageDraw.Draw(im) text = "Some text in arial" draw.text((100, 250), text, font=font, fill=(0, 0, 0)) im = im.resize((256,256), Image.ANTIALIAS) print(ImageFont.getsize(text, font)) mask = ImageFont.getmask(text, font) print(type(mask)) cv2.imshow("mask", mask) im.show() im_numpy = im.getim() print(type(im_numpy), im_numpy.shape, im_numpy.dtype)

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# -*- coding: utf-8 -* import numpy as np import os import math import matplotlib.pyplot as plt import matplotlib.image as mpimg from scipy import misc SAMPLE_NUM = 10 CLASS_NUM = 40 IMG_SHAPE = (112, 92) scale = 0.5 k = 8 principal_percent = 0.8 def load_faceimg(path_dir, shrink_rate=0.5, train_rate=0.8): sample_k = int(train_rate * SAMPLE_NUM) train_m = int(train_rate * SAMPLE_NUM * CLASS_NUM) test_m = int((1 - train_rate) * SAMPLE_NUM * CLASS_NUM) + 1 shape0 = int(IMG_SHAPE[0] * shrink_rate) shape1 = int(IMG_SHAPE[1] * shrink_rate) train_x = np.zeros((train_m, shape0 * shape1)) train_y = np.zeros(train_m).astype(np.int8) test_x = np.zeros((test_m, shape0 * shape1)) test_y = np.zeros(test_m).astype(np.int8) print train_x.shape, test_x.shape for i in range(CLASS_NUM): face_lable = i + 1 for j in range(SAMPLE_NUM): filename = path_dir + '/s' + str(face_lable) + '/' + str(j + 1) + '.pgm' img = misc.imresize(mpimg.imread(filename), shrink_rate).flatten().astype(np.float) if j < sample_k: train_x[i * sample_k + j, :] = img train_y[i * sample_k + j] = face_lable if j >= sample_k: test_x[i * (10 - sample_k) + (j - sample_k), :] = img test_y[i * (10 - sample_k) + (j - sample_k)] = face_lable return train_x, train_y, test_x, test_y # 0均值化 def zero_mean(train_x, test_x): mean_x = train_x.mean(axis = 0).reshape(1, train_x.shape[1]) train_x = train_x - np.repeat(mean_x, train_x.shape[0], axis = 0) test_x = test_x - np.repeat(mean_x, test_x.shape[0], axis=0) return train_x, test_x # PCA降维 def pca(train_x, test_x, threshold): # step1.零均值化 train_x, test_x = zero_mean(train_x, test_x) # step2.协方差矩阵 cov = np.cov(train_x, rowvar=0) # step3.求特征值、特征向量并排序，以及贡献率对应的n值 eig_vals, eig_vecs = np.linalg.eig(cov) n = threshold_trans(eig_vals, threshold) eig = np.vstack((eig_vals, eig_vecs)) eig_vecs = np.delete(eig.T[np.lexsort(eig[::-1, :])].T[:, ::-1], 0, axis=0) # step4.选择前n个特征向量作为基，降维 # n = int(eig_vecs.shape[1]*principal_percent) eig_vecs = eig_vecs[:, 0:n] train_x = np.dot(train_x, eig_vecs) test_x = np.dot(test_x, eig_vecs) return train_x, test_x, eig_vecs def threshold_trans(values, ths): all_values = sum(values) sorted_values = np.sort(values) sorted_values = sorted_values[-1::-1] part_values = 0 n = 0 for value in sorted_values: part_values += value n += 1 if part_values >= all_values * ths: return n def predict(train_x, train_y, test_x, test_y): # recognise via measuring educlidean distance in high dimentional space count = 0 for i in range(test_x.shape[0]): test_x1 = test_x[i, :].reshape((1, test_x.shape[1])) sub = train_x - np.repeat(test_x1, train_x.shape[0], axis=0) dis = np.linalg.norm(sub, axis=1) fig = np.argmin(dis) # print i, train_y[fig], test_y[i] if train_y[fig] == test_y[i]: count += 1 return count def plot_face(img): plt.figure('low dimension map') r, c = (4, 10) for i in range(r * c): plt.subplot(r, c, i + 1) x = int(math.sqrt(img.shape[1])) plt.imshow(img[:, i].real.reshape(int(112*0.5), int(92*0.5)), cmap='gray') plt.axis('off') plt.show() threshold = [0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.999, 0.999999] # 载入数据集 print '[INFO]loading...' train_xs, train_y, test_xs, test_y = load_faceimg(os.getcwd() + '/data') # pca降维 print '[INFO]PCA...' for ths in threshold: train_x, test_x, eig_vecs = pca(train_xs, test_xs, ths) print ths, train_x.shape # 预测 count = predict(train_x, train_y, test_x, test_y) correct_rate = count * 1.0 / test_x.shape[0] print "Correct rate =", correct_rate * 100, "%" if train_x.shape[1] > 40: plot_face(eig_vecs)

[ "632679697@qq.com" ]

632679697@qq.com

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/backend/manage.py

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crowdbotics-apps/u-app-20291

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refs/heads/master

2022-12-12T23:12:59.718116

2020-09-15T17:29:52

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#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'u_app_20291.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

[ "team@crowdbotics.com" ]

team@crowdbotics.com

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/problem_python/p643.py

f23a17357f6d483556d9499124308313657f5eea

[]

no_license

xionghhcs/leetcode

972e7ae4ca56b7100223630b294b5a97ba5dd7e8

8bd43dcd995a9de0270b8cea2d9a48df17ffc08b

refs/heads/master

2020-03-07T17:18:08.465559

2019-09-29T11:11:26

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class Solution(object): def findMaxAverage(self, nums, k): """ :type nums: List[int] :type k: int :rtype: float """ tmp_sum = sum(nums[:4]) i = 0 ans = tmp_sum for j in range(k, len(nums)): tmp_sum = tmp_sum - nums[i] + nums[j] if tmp_sum > ans: ans = tmp_sum i += 1 return float(ans) / k

[ "xionghhcs@163.com" ]

xionghhcs@163.com

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/tests/storage_adapter_tests/test_jsondb_adapter.py

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kashyap32/ChatterBot

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refs/heads/master

2021-01-21T03:29:17.640369

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2016-05-14T00:13:35

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2016-05-14T15:24:19

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from unittest import TestCase from chatterbot.adapters.storage import JsonDatabaseAdapter from chatterbot.conversation import Statement, Response class JsonAdapterTestCase(TestCase): def setUp(self): """ Instantiate the adapter. """ from random import randint # Generate a random name for the database database_name = str(randint(0, 9000)) self.adapter = JsonDatabaseAdapter(database=database_name) def tearDown(self): """ Remove the test database. """ self.adapter.drop() class JsonDatabaseAdapterTestCase(JsonAdapterTestCase): def test_count_returns_zero(self): """ The count method should return a value of 0 when nothing has been saved to the database. """ self.assertEqual(self.adapter.count(), 0) def test_count_returns_value(self): """ The count method should return a value of 1 when one item has been saved to the database. """ statement = Statement("Test statement") self.adapter.update(statement) self.assertEqual(self.adapter.count(), 1) def test_statement_not_found(self): """ Test that None is returned by the find method when a matching statement is not found. """ self.assertEqual(self.adapter.find("Non-existant"), None) def test_statement_found(self): """ Test that a matching statement is returned when it exists in the database. """ statement = Statement("New statement") self.adapter.update(statement) found_statement = self.adapter.find("New statement") self.assertNotEqual(found_statement, None) self.assertEqual(found_statement.text, statement.text) def test_update_adds_new_statement(self): statement = Statement("New statement") self.adapter.update(statement) statement_found = self.adapter.find("New statement") self.assertNotEqual(statement_found, None) self.assertEqual(statement_found.text, statement.text) def test_update_modifies_existing_statement(self): statement = Statement("New statement") self.adapter.update(statement) # Check the initial values found_statement = self.adapter.find(statement.text) self.assertEqual( len(found_statement.in_response_to), 0 ) # Update the statement value statement.add_response( Statement("New response") ) self.adapter.update(statement) # Check that the values have changed found_statement = self.adapter.find(statement.text) self.assertEqual( len(found_statement.in_response_to), 1 ) def test_get_random_returns_statement(self): statement = Statement("New statement") self.adapter.update(statement) random_statement = self.adapter.get_random() self.assertEqual(random_statement.text, statement.text) def test_find_returns_nested_responses(self): response_list = [ Response("Yes"), Response("No") ] statement = Statement( "Do you like this?", in_response_to=response_list ) self.adapter.update(statement) result = self.adapter.find(statement.text) self.assertIn("Yes", result.in_response_to) self.assertIn("No", result.in_response_to) def test_multiple_responses_added_on_update(self): statement = Statement( "You are welcome.", in_response_to=[ Response("Thank you."), Response("Thanks.") ] ) self.adapter.update(statement) result = self.adapter.find(statement.text) self.assertEqual(len(result.in_response_to), 2) self.assertIn(statement.in_response_to[0], result.in_response_to) self.assertIn(statement.in_response_to[1], result.in_response_to) def test_update_saves_statement_with_multiple_responses(self): statement = Statement( "You are welcome.", in_response_to=[ Response("Thank you."), Response("Thanks."), ] ) self.adapter.update(statement) response = self.adapter.find(statement.text) self.assertEqual(len(response.in_response_to), 2) def test_getting_and_updating_statement(self): statement = Statement("Hi") self.adapter.update(statement) statement.add_response(Response("Hello")) statement.add_response(Response("Hello")) self.adapter.update(statement) response = self.adapter.find(statement.text) self.assertEqual(len(response.in_response_to), 1) self.assertEqual(response.in_response_to[0].occurrence, 2) def test_deserialize_responses(self): response_list = [ {"text": "Test", "occurrence": 3}, {"text": "Testing", "occurrence": 1}, ] results = self.adapter.deserialize_responses(response_list) self.assertEqual(len(results), 2) def test_remove(self): text = "Sometimes you have to run before you can walk." statement = Statement(text) self.adapter.update(statement) self.adapter.remove(statement.text) result = self.adapter.find(text) self.assertIsNone(result) def test_remove_response(self): text = "Sometimes you have to run before you can walk." statement = Statement( "A test flight is not recommended at this design phase.", in_response_to=[Response(text)] ) self.adapter.update(statement) self.adapter.remove(statement.text) results = self.adapter.filter(in_response_to__contains=text) self.assertEqual(results, []) class JsonDatabaseAdapterFilterTestCase(JsonAdapterTestCase): def setUp(self): super(JsonDatabaseAdapterFilterTestCase, self).setUp() self.statement1 = Statement( "Testing...", in_response_to=[ Response("Why are you counting?") ] ) self.statement2 = Statement( "Testing one, two, three.", in_response_to=[ Response("Testing...") ] ) def test_filter_text_no_matches(self): self.adapter.update(self.statement1) results = self.adapter.filter(text="Howdy") self.assertEqual(len(results), 0) def test_filter_in_response_to_no_matches(self): self.adapter.update(self.statement1) results = self.adapter.filter( in_response_to=[Response("Maybe")] ) self.assertEqual(len(results), 0) def test_filter_equal_results(self): statement1 = Statement( "Testing...", in_response_to=[] ) statement2 = Statement( "Testing one, two, three.", in_response_to=[] ) self.adapter.update(statement1) self.adapter.update(statement2) results = self.adapter.filter(in_response_to=[]) self.assertEqual(len(results), 2) self.assertIn(statement1, results) self.assertIn(statement2, results) def test_filter_contains_result(self): self.adapter.update(self.statement1) self.adapter.update(self.statement2) results = self.adapter.filter( in_response_to__contains="Why are you counting?" ) self.assertEqual(len(results), 1) self.assertIn(self.statement1, results) def test_filter_contains_no_result(self): self.adapter.update(self.statement1) results = self.adapter.filter( in_response_to__contains="How do you do?" ) self.assertEqual(results, []) def test_filter_multiple_parameters(self): self.adapter.update(self.statement1) self.adapter.update(self.statement2) results = self.adapter.filter( text="Testing...", in_response_to__contains="Why are you counting?" ) self.assertEqual(len(results), 1) self.assertIn(self.statement1, results) def test_filter_multiple_parameters_no_results(self): self.adapter.update(self.statement1) self.adapter.update(self.statement2) results = self.adapter.filter( text="Test", in_response_to__contains="Not an existing response." ) self.assertEqual(len(results), 0) def test_filter_no_parameters(self): """ If no parameters are passed to the filter, then all statements should be returned. """ statement1 = Statement("Testing...") statement2 = Statement("Testing one, two, three.") self.adapter.update(statement1) self.adapter.update(statement2) results = self.adapter.filter() self.assertEqual(len(results), 2) def test_filter_returns_statement_with_multiple_responses(self): statement = Statement( "You are welcome.", in_response_to=[ Response("Thanks."), Response("Thank you.") ] ) self.adapter.update(statement) response = self.adapter.filter( in_response_to__contains="Thanks." ) # Get the first response response = response[0] self.assertEqual(len(response.in_response_to), 2) def test_response_list_in_results(self): """ If a statement with response values is found using the filter method, they should be returned as response objects. """ statement = Statement( "The first is to help yourself, the second is to help others.", in_response_to=[ Response("Why do people have two hands?") ] ) self.adapter.update(statement) found = self.adapter.filter(text=statement.text) self.assertEqual(len(found[0].in_response_to), 1) self.assertEqual(type(found[0].in_response_to[0]), Response) class ReadOnlyJsonDatabaseAdapterTestCase(JsonAdapterTestCase): def test_update_does_not_add_new_statement(self): self.adapter.read_only = True statement = Statement("New statement") self.adapter.update(statement) statement_found = self.adapter.find("New statement") self.assertEqual(statement_found, None) def test_update_does_not_modify_existing_statement(self): statement = Statement("New statement") self.adapter.update(statement) self.adapter.read_only = True statement.add_response( Statement("New response") ) self.adapter.update(statement) statement_found = self.adapter.find("New statement") self.assertEqual(statement_found.text, statement.text) self.assertEqual( len(statement_found.in_response_to), 0 )

[ "gunthercx@gmail.com" ]

gunthercx@gmail.com

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/planner.py

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[]

no_license

mikesuhan/canvas_automation

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refs/heads/main

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import datetime from dateutil.parser import parse def date_range(first_day=datetime.datetime(2021, 1, 11, 8, 30), last_day=datetime.datetime(2021, 5, 7, 8, 30)): delta = last_day - first_day return list(reversed([last_day - datetime.timedelta(days=x) for x in range(delta.days + 1)])) def session_range(dates, *times, holidays=('jan 18 2020',)): """ Filters a range of dates based on session times Arguments: dates: a list of datetime objects *times: a tuple of the day, start time, and end time of classes e.g. ('Monday', '8am', '10am' Keyword Arguments: holidays: a tuple of strings of holiday dates -- these dates are not included in the output """ sessions = [] if holidays is None: holidays = [] for date in dates: # checks to make sure date isn't a holiday for holiday in holidays: if type(holiday) == str: holiday = parse(holiday) if holiday.day == date.day and holiday.month == date.month and holiday.year == holiday.year: break # continues if date is not a holiday else: day = date.strftime("%a").lower() for session in times: d, ts = session[0], session[1:] if d.lower().startswith(day): start_t = parse(ts[0]) start_at = date.replace(hour=start_t.hour, minute=start_t.minute) if len(ts) > 1: end_t = parse(ts[1]) end_at = date.replace(hour=end_t.hour, minute=end_t.minute) else: end_at = None sessions.append((start_at, end_at)) return sessions

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you@example.com

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/apps/user_operation/migrations/0004_auto_20181204_0942.py

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[]

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vevoly/ShopDjango

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# -*- coding: utf-8 -*- # Generated by Django 1.11 on 2018-12-04 09:42 from __future__ import unicode_literals import datetime from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('user_operation', '0003_auto_20181117_1121'), ] operations = [ migrations.AlterField( model_name='useraddress', name='add_time', field=models.DateTimeField(default=datetime.datetime(2018, 12, 4, 9, 42, 7, 105424), help_text='添加时间', verbose_name='添加时间'), ), migrations.AlterField( model_name='userfav', name='add_time', field=models.DateTimeField(default=datetime.datetime(2018, 12, 4, 9, 42, 7, 103424), help_text='添加时间', verbose_name='添加时间'), ), migrations.AlterField( model_name='userleavingmessage', name='add_time', field=models.DateTimeField(default=datetime.datetime(2018, 12, 4, 9, 42, 7, 104424), help_text='添加时间', verbose_name='添加时间'), ), ]

[ "jevoly@163.com" ]

jevoly@163.com

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/xai/brain/wordbase/verbs/_blitzes.py

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cash2one/xai

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refs/heads/master

2021-01-19T12:33:54.964379

2017-01-28T02:00:50

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from xai.brain.wordbase.verbs._blitz import _BLITZ #calss header class _BLITZES(_BLITZ, ): def __init__(self,): _BLITZ.__init__(self) self.name = "BLITZES" self.specie = 'verbs' self.basic = "blitz" self.jsondata = {}

[ "xingwang1991@gmail.com" ]

xingwang1991@gmail.com

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/HSTB/kluster/gui/kluster_swathview.py

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refs/heads/master

2023-03-15T14:22:51.569255

2021-03-18T21:27:11

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# !/usr/bin/env python # -*- coding: utf-8 -*- # vispy: testskip # ----------------------------------------------------------------------------- # 2016, Scott Paine # Distributed under the terms of the new BSD License. # ----------------------------------------------------------------------------- """ ********** Wiggly Bar ********** Usage of VisPy to numerically simulate and view a simple physics model. .. image:: http://i.imgur.com/ad0s9lB.png This is a simple example of using VisPy to simulate a system with two springs, a pivot, and a mass. The system evolves in a nonlinear fashion, according to two equations: .. image:: http://i.imgur.com/8reci4N.png In these equations, the J term is the polar moment of inertia of the rod given by: .. image:: http://i.imgur.com/94cI1TL.png The system has the option to update once every step using the `Euler <https://en.wikipedia.org/wiki/Euler_method>`_ method or a more stable third-order `Runge-Kutta <https://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods>`_ method. The instability of the Euler Method becomes apparent as the time step is increased. """ from __future__ import division, print_function, absolute_import from vispy import app, visuals from vispy.visuals import transforms from vispy.io import load_data_file import sys import numpy as np import string import logging import traceback # To switch between PyQt5 and PySide2 bindings just change the from import from HSTB.kluster.gui.backends._qt import QtGui, QtCore, QtWidgets, Signal logger = logging.getLogger(__name__) VALID_METHODS = ['euler', 'runge-kutta'] PARAMETERS = [('d1', 0.0, 10.0, 'double', 0.97), ('d2', 0.0, 10.0, 'double', 0.55), ('m', 0.01, 100.0, 'double', 2.0), ('M', 0.01, 100.0, 'double', 12.5), ('k1', 0.01, 75.0, 'double', 1.35), ('k2', 0.01, 75.0, 'double', 0.50), ('b', 1.0, 1000.0, 'double', 25.75), ('time step', 0.001, 1.0, 'double', 1 / 60), ('x', -0.25, 0.25, 'double', -0.01), ('x dot', -10.0, 10.0, 'double', -0.12), ('theta', -np.pi / 5, np.pi / 5, 'double', 0.005), ('theta dot', -np.pi / 2, np.pi / 2, 'double', 0.0), ('scale', 5, 500, 'int', 50), ('font size', 6.0, 128.0, 'double', 24.0)] CONVERSION_DICT = {'d1': 'd1', 'd2': 'd2', 'm': 'little_m', 'M': 'big_m', 'k1': 'spring_k1', 'k2': 'spring_k2', 'b': 'b', 'x': 'x', 'x dot': 'x_dot', 'theta': 'theta', 'theta dot': 'theta_dot', 'scale': 'scale', 'time step': 'dt', 'font size': 'font_size'} def make_spiral(num_points=100, num_turns=4, height=12, radius=2.0, xnot=None, ynot=None, znot=None): """ Generate a list of points corresponding to a spiral. Parameters ---------- num_points : int Number of points to map spiral over. More points means a rounder spring. num_turns : int Number of coils in the spiral height : float The height of the spiral. Keep it in whatever units the rest of the spiral is in. radius : float The radius of the coils. The spiral will end up being 2*radius wide. xnot : float Initial x-coordinate for the spiral coordinates to start at. ynot : float Initial y-coordinate for the spiral coordinates to start at. znot : float Initial z-coordinate for the spiral coordinates to start at. Returns ------- coord_list: list of tuples Coordinate list of (x, y, z) positions for the spiral Notes ----- Right now, this assumes the center is at x=0, y=0. Later, it might be good to add in stuff to change that. """ coords_list = [] znot = -4 if znot is None else znot xnot = radius if xnot is None else xnot ynot = 0 if ynot is None else ynot theta_not = np.arctan2(ynot, xnot) coords_list.append((xnot, ynot, znot)) for point in range(num_points): znot += height / num_points theta_not += 2 * np.pi * num_turns / num_points xnot = np.cos(theta_not) * radius ynot = np.sin(theta_not) * radius coords_list.append((xnot, ynot, znot)) return coords_list def make_spring(num_points=300, num_turns=4, height=12, radius=2.0, xnot=None, ynot=None, znot=None): """ Generate a list of points corresponding to a spring. Parameters ---------- num_points : int Number of points to map spring over. More points means a rounder spring. num_turns : int Number of coils in the spring height : float The height of the spring. Keep it in whatever units the rest of the spring is in. radius : float The radius of the coils. The spring will end up being 2*radius wide. xnot : float Initial x-coordinate for the spring coordinates to start at. ynot : float Initial y-coordinate for the spring coordinates to start at. znot : float Initial z-coordinate for the spring coordinates to start at. Returns ------- coord_list: list of tuples Coordinate list of (x, y, z) positions for the spring Notes ----- Right now, this assumes the center is at x=0, y=0. Later, it might be good to add in stuff to change that. Right now, the length of the "ends" is 10% of the overall length, as well as a small "turn" that is length radius / 2. In the future, maybe there could be a kwarg to set the length of the sides of the spring. For now, 10% looks good. """ coords_list = [] init_pts = num_points // 10 znot = 0 if znot is None else znot xnot = 0 if xnot is None else xnot ynot = 0 if ynot is None else ynot coords_list.append((xnot, ynot, znot)) for _ in range(init_pts): znot += height / num_points coords_list.append((xnot, ynot, znot)) hold_z = znot for i in range(init_pts // 2): small_theta = (i + 1) * np.pi / init_pts xnot = radius / 2 * (1 - np.cos(small_theta)) znot = hold_z + radius / 2 * np.sin(small_theta) coords_list.append((xnot, ynot, znot)) coords_list += make_spiral(num_points=num_points - 3 * init_pts, num_turns=num_turns, height=( height - (91 * height / num_points) - radius / 2 ), radius=radius, xnot=xnot, ynot=ynot, znot=znot) hold_z = coords_list[-1][-1] for i in range(init_pts // 2): small_theta = np.pi / 2 - (i + 1) * np.pi / init_pts xnot = radius / 2 * (1 - np.cos(small_theta)) znot = hold_z + radius / 2 * np.cos(small_theta) coords_list.append((xnot, ynot, znot)) xnot = 0.0 znot += height / num_points for _ in range(init_pts): znot += height / num_points coords_list.append((xnot, ynot, znot)) coords_list.append((0, 0, height)) return coords_list class WigglyBar(app.Canvas): def __init__(self, d1=None, d2=None, little_m=None, big_m=None, spring_k1=None, spring_k2=None, b=None, x=None, x_dot=None, theta=None, theta_dot=None, px_len=None, scale=None, pivot=False, method='Euler', dt=None, font_size=None): """ Main VisPy Canvas for simulation of physical system. Parameters ---------- d1 : float Length of rod (in meters) from pivot to upper spring. d2 : float Length of rod (in meters) from pivot to lower spring. little_m : float Mass of attached cube (in kilograms). big_m : float Mass of rod (in kilograms). spring_k1 : float Spring constant of lower spring (in N/m). spring_k2 : float Spring constant of upper spring (in N/m). b : float Coefficient of quadratic sliding friction (in kg/m). x : float Initial x-position of mass (in m). x_dot : float Initial x-velocity of mass (in m/s). theta : float Initial angle of rod, with respect to vertical (in radians). theta_dot : float Initial angular velocity of rod (in rad/s). px_len : int Length of the rod, in pixels. scale : int Scaling factor to change size of elements. pivot : bool Switch for showing/hiding pivot point. method : str Method to use for updating. dt : float Time step for simulation. font_size : float Size of font for text elements, in points. Notes ----- As of right now, the only supported methods are "euler" or "runge-kutta". These correspond to an Euler method or an order 3 Runge-Kutta method for updating x, theta, x dot, and theta dot. """ app.Canvas.__init__(self, title='Wiggly Bar', size=(800, 800), create_native=False) # Some initialization constants that won't change self.standard_length = 0.97 + 0.55 self.center = np.asarray((500, 450)) self.visuals = [] self._set_up_system( d1=d1, d2=d2, little_m=little_m, big_m=big_m, spring_k1=spring_k1, spring_k2=spring_k2, b=b, x=x, x_dot=x_dot, theta=theta, theta_dot=theta_dot, px_len=px_len, scale=scale, pivot=pivot, method=method, dt=dt, font_size=font_size ) piv_x_y_px = np.asarray(( self.pivot_loc_px * np.sin(self.theta), -1 * self.pivot_loc_px * (np.cos(self.theta)) )) # Make the spring points points = make_spring(height=self.px_len / 4, radius=self.px_len / 24) # Put up a text visual to display time info self.font_size = 24. if font_size is None else font_size self.text = visuals.TextVisual('0:00.00', color='white', pos=[50, 250, 0], anchor_x='left', anchor_y='bottom') self.text.font_size = self.font_size # Let's put in more text so we know what method is being used to # update this self.method_text = visuals.TextVisual( 'Method: {}'.format(self.method), color='white', pos=[50, 250, 0], anchor_x='left', anchor_y='top' ) self.method_text.font_size = 2 / 3 * self.font_size # Get the pivoting bar ready self.rod = visuals.BoxVisual(width=self.px_len / 40, height=self.px_len / 40, depth=self.px_len, color='white') self.rod.transform = transforms.MatrixTransform() self.rod.transform.scale( (self.scale, self.scale * self.rod_scale, 0.0001) ) self.rod.transform.rotate(np.rad2deg(self.theta), (0, 0, 1)) self.rod.transform.translate(self.center - piv_x_y_px) # Show the pivot point (optional) pivot_center = (self.center[0], self.center[1], -self.px_len / 75) self.center_point = visuals.SphereVisual(radius=self.px_len / 75, color='red') self.center_point.transform = transforms.MatrixTransform() self.center_point.transform.scale((self.scale, self.scale, 0.0001)) self.center_point.transform.translate(pivot_center) # Get the upper spring ready. self.spring_2 = visuals.TubeVisual( points, radius=self.px_len / 100, color=(0.5, 0.5, 1, 1) ) self.spring_2.transform = transforms.MatrixTransform() self.spring_2.transform.rotate(90, (0, 1, 0)) self.spring_2.transform.scale((self.scale, self.scale, 0.0001)) self.spring_2.transform.translate(self.center + self.s2_loc) # Get the lower spring ready. self.spring_1 = visuals.TubeVisual( points, radius=self.px_len / 100, color=(0.5, 0.5, 1, 1) ) self.spring_1.transform = transforms.MatrixTransform() self.spring_1.transform.rotate(90, (0, 1, 0)) self.spring_1.transform.scale( ( self.scale * (1.0 - (self.x * self.px_per_m) / (self.scale * self.px_len / 2)), self.scale, 0.0001 ) ) self.spring_1.transform.translate(self.center + self.s1_loc) # Finally, prepare the mass that is being moved self.mass = visuals.BoxVisual( width=self.px_len / 4, height=self.px_len / 8, depth=self.px_len / 4, color='white' ) self.mass.transform = transforms.MatrixTransform() self.mass.transform.scale((self.scale, self.scale, 0.0001)) self.mass.transform.translate(self.center + self.mass_loc) # Append all the visuals self.visuals.append(self.center_point) self.visuals.append(self.rod) self.visuals.append(self.spring_2) self.visuals.append(self.spring_1) self.visuals.append(self.mass) self.visuals.append(self.text) self.visuals.append(self.method_text) # Set up a timer to update the image and give a real-time rendering self._timer = app.Timer('auto', connect=self.on_timer, start=True) def on_draw(self, ev): # Stolen from previous - just clears the screen and redraws stuff self.context.set_clear_color((0, 0, 0, 1)) self.context.set_viewport(0, 0, *self.physical_size) self.context.clear() for vis in self.visuals: if vis is self.center_point and not self.show_pivot: continue else: vis.draw() def on_resize(self, event): # Set canvas viewport and reconfigure visual transforms to match. vp = (0, 0, self.physical_size[0], self.physical_size[1]) self.context.set_viewport(*vp) for vis in self.visuals: vis.transforms.configure(canvas=self, viewport=vp) def on_timer(self, ev): # Update x, theta, x_dot, theta_dot self.params_update(dt=self.dt, method=self.method) # Calculate change for the upper spring, relative to its starting point extra_term = self.theta - self.theta_not trig_junk = ( np.sin(self.theta_not) * (np.cos(extra_term) - 1) + np.cos(self.theta_not) * np.sin(extra_term) ) delta_x = self.d1 * self.px_per_m * trig_junk net_s2_scale = (1 - (delta_x / (self.scale * self.px_len / 4))) # Calculate change for the lower spring, relative to something # arbitrary so I didn't have horrors mathematically trig_junk_2 = np.sin(self.theta_not) - np.sin(self.theta) first_term = self.d2 * trig_junk_2 top_term = (first_term - self.x) * self.px_per_m net_s1_scale = 1 + top_term / self.s1_l_not self.s1_loc[0] = -0.5 * ( -self.x * self.px_per_m + self.s1_l_not + self.d2 * self.px_per_m * (np.sin(self.theta) + np.sin(self.theta_not)) ) self.s1_loc[0] -= 0.5 * net_s1_scale * self.s1_l_not # Calculate the new pivot location - this is important because the # rotation occurs about # the center of the rod, so it has to be offset appropriately piv_x_y_px = np.asarray(( self.pivot_loc_px * np.sin(self.theta), -1 * self.pivot_loc_px * np.cos(self.theta) )) # Calculate the new mass x location, relative (again) to some # simple parameter where x=0 self.mass_loc[0] = self.x_is_0 + self.x * self.px_per_m # Figure out how much time has passed millis_passed = int(100 * (self.t % 1)) sec_passed = int(self.t % 60) min_passed = int(self.t // 60) # Apply the necessary transformations to the rod self.rod.transform.reset() self.rod.transform.scale( (self.scale, self.scale * self.rod_scale, 0.0001) ) self.rod.transform.rotate(np.rad2deg(self.theta), (0, 0, 1)) self.rod.transform.translate(self.center - piv_x_y_px) # Redraw and rescale the upper spring self.spring_2.transform.reset() self.spring_2.transform.rotate(90, (0, 1, 0)) self.spring_2.transform.scale((net_s2_scale * self.scale, self.scale, 0.0001)) self.spring_2.transform.translate(self.center + self.s2_loc + np.asarray([delta_x, 0])) # Redraw and rescale the lower spring # (the hardest part to get, mathematically) self.spring_1.transform.reset() self.spring_1.transform.rotate(90, (0, 1, 0)) self.spring_1.transform.scale((net_s1_scale * self.scale, self.scale, 0.0001)) self.spring_1.transform.translate(self.center + self.s1_loc) # Redrew and rescale the mass self.mass.transform.reset() self.mass.transform.scale((self.scale, self.scale, 0.0001)) self.mass.transform.translate(self.center + self.mass_loc) # Update the timer with how long it's been self.text.text = '{:0>2d}:{:0>2d}.{:0>2d}'.format(min_passed, sec_passed, millis_passed) # Trigger all of the drawing and updating self.update() def params_update(self, dt, method='euler'): # Uses either Euler method or Runge-Kutta, # depending on your input to "method" if method.lower() == 'euler': self._euler_update(dt) elif method.lower() == 'runge-kutta': self._runge_kutta_update(dt) def _euler_update(self, dt): """Update system using Euler's method (equivalent to order 1 Runge-Kutta Method). """ # Calculate the second derivative of x x_dd_t1 = -self.b * self.x_dot * np.abs(self.x_dot) x_dd_t2 = -self.spring_k1 * (self.x + self.d2 * self.theta) x_dot_dot = (x_dd_t1 + x_dd_t2) / self.little_m # Calculate the second derivative of theta term1 = -self.spring_k1 * self.d2 * self.x term2 = ( -self.theta * (self.spring_k1 * (self.d2 ** 2) + self.spring_k2 * (self.d1 ** 2)) ) theta_dot_dot = (term1 + term2) / self.j_term # Update everything appropriately self.t += dt self.x += dt * self.x_dot self.theta += dt * self.theta_dot self.x_dot += dt * x_dot_dot self.theta_dot += dt * theta_dot_dot def _runge_kutta_update(self, dt): """Update using order 3 Runge-Kutta Method. """ info_vector = np.asarray( [self.x_dot, self.theta_dot, self.x, self.theta] ).copy() t1a = -self.b * info_vector[0] * np.abs(info_vector[0]) t1b = -self.spring_k1 * (info_vector[2] + self.d2 * info_vector[3]) t2a = -self.spring_k1 * self.d2 * info_vector[2] t2b = -info_vector[3] * ( self.spring_k1 * (self.d2 ** 2) + self.spring_k2 * (self.d1 ** 2) ) k1 = [ (t1a + t1b) / self.little_m, (t2a + t2b) / self.j_term, info_vector[0], info_vector[1] ] k1 = np.asarray(k1) * dt updated_est = info_vector + 0.5 * k1 t1a = -self.b * updated_est[0] * np.abs(updated_est[0]) t1b = -self.spring_k1 * (updated_est[2] + self.d2 * updated_est[3]) t2a = -self.spring_k1 * self.d2 * updated_est[2] t2b = -updated_est[3] * ( self.spring_k1 * (self.d2 ** 2) + self.spring_k2 * (self.d1 ** 2) ) k2 = [ (t1a + t1b) / self.little_m, (t2a + t2b) / self.j_term, updated_est[0], updated_est[1] ] k2 = np.asarray(k2) * dt updated_est = info_vector - k1 + 2 * k2 t1a = -self.b * updated_est[0] * np.abs(updated_est[0]) t1b = -self.spring_k1 * (updated_est[2] + self.d2 * updated_est[3]) t2a = -self.spring_k1 * self.d2 * updated_est[2] t2b = -updated_est[3] * ( self.spring_k1 * (self.d2 ** 2) + self.spring_k2 * (self.d1 ** 2) ) k3 = [ (t1a + t1b) / self.little_m, (t2a + t2b) / self.j_term, updated_est[0], updated_est[1] ] k3 = np.asarray(k3) * dt final_est = info_vector + (1 / 6) * (k1 + 4 * k2 + k3) self.x_dot, self.theta_dot, self.x, self.theta = final_est.copy() self.t += dt def reset_parms(self, d1=None, d2=None, little_m=None, big_m=None, spring_k1=None, spring_k2=None, b=None, x=None, x_dot=None, theta=None, theta_dot=None, px_len=None, scale=None, pivot=False, method='Euler', dt=None, font_size=None): """ Reset system with a new set of paramters. Parameters ---------- d1 : float Length of rod (in meters) from pivot to upper spring. d2 : float Length of rod (in meters) from pivot to lower spring. little_m : float Mass of attached cube (in kilograms). big_m : float Mass of rod (in kilograms). spring_k1 : float Spring constant of lower spring (in N/m). spring_k2 : float Spring constant of upper spring (in N/m). b : float Coefficient of quadratic sliding friction (in kg/m). x : float Initial x-position of mass (in m). x_dot : float Initial x-velocity of mass (in m/s). theta : float Initial angle of rod, with respect to vertical (in radians). theta_dot : float Initial angular velocity of rod (in rad/s). px_len : int Length of the rod, in pixels. scale : int Scaling factor to change size of elements. pivot : bool Switch for showing/hiding pivot point. method : str Method to use for updating. dt : float Time step for simulation. font_size : float Size of font for text elements, in points. Notes ----- Since the time is reset, the system is reset as well by calling this method. """ self._set_up_system( d1=d1, d2=d2, little_m=little_m, big_m=big_m, spring_k1=spring_k1, spring_k2=spring_k2, b=b, x=x, x_dot=x_dot, theta=theta, theta_dot=theta_dot, px_len=px_len, scale=scale, pivot=pivot, method=method, dt=dt, font_size=font_size ) def _set_up_system(self, d1=None, d2=None, little_m=None, big_m=None, spring_k1=None, spring_k2=None, b=None, x=None, x_dot=None, theta=None, theta_dot=None, px_len=None, scale=None, pivot=False, method='Euler', dt=None, font_size=None): """Initialize constants for the system that will be used later. """ self.method = (string.capwords(method, '-') if method.lower() in VALID_METHODS else 'Euler') self.font_size = font_size try: self.method_text.text = 'Method: {}'.format(self.method) self.method_text.font_size = 2 / 3 * self.font_size self.text.font_size = self.font_size except AttributeError: # Running in __init__, so self.method_text isn't established yet. pass self.show_pivot = pivot # Initialize constants for the system self.t = 0 self.dt = 1 / 60 if dt is None else dt self.d1 = 0.97 if d1 is None else d1 self.d2 = 0.55 if d2 is None else d2 self.little_m = 2.0 if little_m is None else little_m self.big_m = 12.5 if big_m is None else big_m self.spring_k1 = 1.35 if spring_k1 is None else spring_k1 self.spring_k2 = 0.5 if spring_k2 is None else spring_k2 self.b = 25.75 if b is None else b self.j_term = ( (1 / 3) * self.big_m * (self.d1 ** 3 + self.d2 ** 3) / (self.d1 + self.d2) ) self.x = -0.010 if x is None else x self.x_dot = -0.12 if x_dot is None else x_dot self.theta = 0.005 if theta is None else theta self.theta_dot = 0.0 if theta_dot is None else theta_dot self.theta_not = self.theta # I'll need this later # Initialize constants for display self.px_len = 10 if px_len is None else px_len self.scale = 50 if scale is None else scale self.px_per_m = self.scale * self.px_len / (0.97 + 0.55) self.rod_scale = (self.d1 + self.d2) / self.standard_length # Set up stuff for establishing a pivot point to rotate about self.pivot_loc = (self.d2 - self.d1) / 2 self.pivot_loc_px = self.pivot_loc * self.px_per_m # Set up positioning info for the springs and mass, as well as some # constants for use later # NOTE: Springs are not like boxes. Their center of rotation is at one # end of the spring, unlike the box where it is in the middle. # The location and scaling is set to reflect this. This means # there's a little bit of x- and y-translation needed to properly # center them. self.s2_loc = np.asarray( [self.d1 * self.px_per_m * np.sin(self.theta), -self.d1 * self.px_per_m * np.cos( self.theta)] ) self.s1_l_not = self.px_len / 4 * self.scale self.x_is_0 = ( -self.d2 * self.px_per_m * np.sin(self.theta_not) - 1.5 * self.s1_l_not ) self.s1_loc = np.asarray( [self.x_is_0 + 0.5 * self.s1_l_not + self.x * self.px_per_m, self.d2 * self.px_per_m * np.cos(self.theta)] ) self.mass_loc = np.asarray( [self.x_is_0 + self.x * self.px_per_m, self.d2 * self.px_per_m * np.cos(self.theta)] ) class Paramlist(object): def __init__(self, parameters): """Container for object parameters. Based on methods from ../gloo/primitive_mesh_viewer_qt. """ self.parameters = parameters self.props = dict() self.props['pivot'] = False self.props['method'] = 'Euler' for nameV, minV, maxV, typeV, iniV in parameters: nameV = CONVERSION_DICT[nameV] self.props[nameV] = iniV class SetupWidget(QtWidgets.QWidget): changed_parameter_sig = Signal(Paramlist) def __init__(self, parent=None): """Widget for holding all the parameter options in neat lists. Based on methods from ../gloo/primitive_mesh_viewer_qt. """ super(SetupWidget, self).__init__(parent) # Create the parameter list from the default parameters given here self.param = Paramlist(PARAMETERS) # Checkbox for whether or not the pivot point is visible self.pivot_chk = QtWidgets.QCheckBox(u"Show pivot point") self.pivot_chk.setChecked(self.param.props['pivot']) self.pivot_chk.toggled.connect(self.update_parameters) # A drop-down menu for selecting which method to use for updating self.method_list = ['Euler', 'Runge-Kutta'] self.method_options = QtWidgets.QComboBox() self.method_options.addItems(self.method_list) self.method_options.setCurrentIndex( self.method_list.index((self.param.props['method'])) ) self.method_options.currentIndexChanged.connect( self.update_parameters ) # Separate the different parameters into groupboxes, # so there's a clean visual appearance self.parameter_groupbox = QtWidgets.QGroupBox(u"System Parameters") self.conditions_groupbox = QtWidgets.QGroupBox(u"Initial Conditions") self.display_groupbox = QtWidgets.QGroupBox(u"Display Parameters") self.groupbox_list = [self.parameter_groupbox, self.conditions_groupbox, self.display_groupbox] self.splitter = QtWidgets.QSplitter(QtCore.Qt.Vertical) # Get ready to create all the spinboxes with appropriate labels plist = [] self.psets = [] # important_positions is used to separate the # parameters into their appropriate groupboxes important_positions = [0, ] param_boxes_layout = [QtWidgets.QGridLayout(), QtWidgets.QGridLayout(), QtWidgets.QGridLayout()] for nameV, minV, maxV, typeV, iniV in self.param.parameters: # Create Labels for each element plist.append(QtWidgets.QLabel(nameV)) if nameV == 'x' or nameV == 'scale': # 'x' is the start of the 'Initial Conditions' groupbox, # 'scale' is the start of the 'Display Parameters' groupbox important_positions.append(len(plist) - 1) # Create Spinboxes based on type - doubles get a DoubleSpinBox, # ints get regular SpinBox. # Step sizes are the same for every parameter except font size. if typeV == 'double': self.psets.append(QtWidgets.QDoubleSpinBox()) self.psets[-1].setDecimals(3) if nameV == 'font size': self.psets[-1].setSingleStep(1.0) else: self.psets[-1].setSingleStep(0.01) elif typeV == 'int': self.psets.append(QtWidgets.QSpinBox()) # Set min, max, and initial values self.psets[-1].setMaximum(maxV) self.psets[-1].setMinimum(minV) self.psets[-1].setValue(iniV) pidx = -1 for pos in range(len(plist)): if pos in important_positions: pidx += 1 param_boxes_layout[pidx].addWidget(plist[pos], pos + pidx, 0) param_boxes_layout[pidx].addWidget(self.psets[pos], pos + pidx, 1) self.psets[pos].valueChanged.connect(self.update_parameters) param_boxes_layout[0].addWidget(QtWidgets.QLabel('Method: '), 8, 0) param_boxes_layout[0].addWidget(self.method_options, 8, 1) param_boxes_layout[-1].addWidget(self.pivot_chk, 2, 0, 3, 0) for groupbox, layout in zip(self.groupbox_list, param_boxes_layout): groupbox.setLayout(layout) for groupbox in self.groupbox_list: self.splitter.addWidget(groupbox) vbox = QtWidgets.QVBoxLayout() hbox = QtWidgets.QHBoxLayout() hbox.addWidget(self.splitter) hbox.addStretch(5) vbox.addLayout(hbox) vbox.addStretch(1) self.setLayout(vbox) def update_parameters(self, option): """When the system parameters change, get the state and emit it.""" self.param.props['pivot'] = self.pivot_chk.isChecked() self.param.props['method'] = self.method_list[ self.method_options.currentIndex() ] keys = map(lambda x: x[0], self.param.parameters) for pos, nameV in enumerate(keys): self.param.props[CONVERSION_DICT[nameV]] = self.psets[pos].value() self.changed_parameter_sig.emit(self.param) class MainWindow(QtWidgets.QMainWindow): def __init__(self, param=None): """Main Window for holding the Vispy Canvas and the parameter control menu. """ QtWidgets.QMainWindow.__init__(self) self.resize(1067, 800) icon = load_data_file('wiggly_bar/spring.ico') self.setWindowIcon(QtGui.QIcon(icon)) self.setWindowTitle('Nonlinear Physical Model Simulation') self.parameter_object = SetupWidget(self) self.parameter_object.param = (param if param is not None else self.parameter_object.param) self.parameter_object.changed_parameter_sig.connect(self.update_view) self.view_box = WigglyBar(**self.parameter_object.param.props) self.view_box.create_native() self.view_box.native.setParent(self) splitter = QtWidgets.QSplitter(QtCore.Qt.Horizontal) splitter.addWidget(self.parameter_object) splitter.addWidget(self.view_box.native) self.setCentralWidget(splitter) def update_view(self, param): """Update the VisPy canvas when the parameters change. """ self.view_box.reset_parms(**param.props) def uncaught_exceptions(ex_type, ex_value, ex_traceback): lines = traceback.format_exception(ex_type, ex_value, ex_traceback) msg = ''.join(lines) logger.error('Uncaught Exception\n%s', msg) def main(): sys.excepthook = uncaught_exceptions logging.basicConfig(level=logging.INFO) logging.getLogger().setLevel(logging.INFO) try: # pyside2 app = QtWidgets.QApplication() except TypeError: # pyqt5 app = QtWidgets.QApplication([]) win = MainWindow() win.show() app.exec_() if __name__ == '__main__': main()

[ "eyou102@gmail.com" ]

eyou102@gmail.com

319f8d28ab811b2e7eaf832c142ce5a9f1993d33

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.contrib import admin from book.models import Category, Book, Comment class CategoryAdmin(admin.ModelAdmin): list_display = ('name', ) class BookAdmin(admin.ModelAdmin): list_display = ('category', 'title', 'author', 'price', 'score', 'total_chapter', 'allow_trial', 'trial_chapter', 'create_timestamp', 'update_timestamp') class CommentAdmin(admin.ModelAdmin): list_display = ('user', 'book', 'score', 'content', 'timestamp') admin.site.register(Category, CategoryAdmin) admin.site.register(Book, BookAdmin) admin.site.register(Comment, CommentAdmin)

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import sys sys.stdin = open('input1.txt','r') T=int(input()) for i in range(0, T): day = int(input()) dayprice = list(map(int, input().split())) maxprice = dayprice[len(dayprice)-1] benefit = 0 buy = 0 for j in range(day-2, -1, -1): if dayprice[j] < maxprice: benefit += maxprice-dayprice[j] else: maxprice = dayprice[j] print('#{0} {1}'.format(i+1, benefit)) # for tc in range(1, int(input())+1): # N = int(input()) # costs = list(map(int, input().split())) # # result = 0 # while True: # max_value = max(costs) # max_idx = costs.index(max_value) # total = 0 # if max_idx != 0: # total = max_value * max_idx # for i in range(max_idx): # total -= costs[i] # result += total # # if max_idx == len(costs)-1 or max_idx == len(costs)-2: # break # else: # costs = costs[max_idx+1:] # # print(f'#{tc} {result}')

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# this line will show some book samples in NLTK. from nltk.book import * ## concordance # print('Sense and Sensibility by Jane Austen 1811') # print(text2.concordance('affection')) # print('text5: Chat Corpus') print(text5.concordance('lol')) ## similarity # print(text1.similar('monstrous')) ## common contexts # print(text2.common_contexts(["monstrous", "very"])) ## dispersion plot # text4.dispersion_plot(['citizens', 'democracy', 'freedom', 'duties', 'America']) ## generate is note supported now? # print(text3.generate()) ##

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with open("input.txt", "r") as f, open("output.txt", "w") as q: n, m, k = (int(x) for x in f.read().split()) q.write("1" if n >= m else "NO" if n <= k else str((m - n - 1) // (n - k) + 2))

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from django import template from kontrasto import wcag_2, wcag_3 register = template.Library() @register.filter(name="dominant_color") def dominant_color(image): return image.get_dominant_color() @register.filter(name="wcag_2_contrast") def wcag_2_contrast(image, text_color: str) -> str: return wcag_2.wcag2_contrast(image.get_dominant_color(), text_color) @register.simple_tag(name="wcag_2_contrast_light_or_dark") def wcag_2_contrast_light_or_dark( image, light_color: str, dark_color: str ) -> str: dominant = image.get_dominant_color() light_contrast = wcag_2.wcag2_contrast(dominant, light_color) dark_contrast = wcag_2.wcag2_contrast(dominant, dark_color) lighter = light_contrast > dark_contrast return { "text_color": light_color if lighter else dark_color, "text_theme": "light" if lighter else "dark", "bg_color": dominant, "bg_color_transparent": f"{dominant}aa", "bg_theme": "dark" if lighter else "light", } @register.filter(name="wcag_3_contrast") def wcag_3_contrast(image, text_color: str) -> str: return wcag_3.apca_contrast(image.get_dominant_color(), text_color) @register.simple_tag(name="wcag_3_contrast_light_or_dark") def wcag_3_contrast_light_or_dark( image, light_color: str, dark_color: str ) -> str: dominant = image.get_dominant_color() light_contrast = wcag_3.format_contrast( wcag_3.apca_contrast(dominant, light_color) ) dark_contrast = wcag_3.format_contrast( wcag_3.apca_contrast(dominant, dark_color) ) lighter = light_contrast > dark_contrast return { "text_color": light_color if lighter else dark_color, "text_theme": "light" if lighter else "dark", "bg_color": dominant, "bg_color_transparent": f"{dominant}aa", "bg_theme": "dark" if lighter else "light", }

[ "thibaudcolas@gmail.com" ]

thibaudcolas@gmail.com

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cnsuhao/WOT-0.9.17-CT

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# 2016.11.19 19:51:28 Střední Evropa (běžný čas) # Embedded file name: scripts/client/gui/Scaleform/daapi/view/meta/PremiumWindowMeta.py from gui.Scaleform.daapi.view.meta.SimpleWindowMeta import SimpleWindowMeta class PremiumWindowMeta(SimpleWindowMeta): """ DO NOT MODIFY! Generated with yaml. __author__ = 'yaml_processor' @extends SimpleWindowMeta """ def onRateClick(self, rateId): self._printOverrideError('onRateClick') def as_setHeaderS(self, prc, bonus1, bonus2): if self._isDAAPIInited(): return self.flashObject.as_setHeader(prc, bonus1, bonus2) def as_setRatesS(self, data): """ :param data: Represented by PremiumWindowRatesVO (AS) """ if self._isDAAPIInited(): return self.flashObject.as_setRates(data) # okay decompyling c:\Users\PC\wotsources\files\originals\res\scripts\client\gui\Scaleform\daapi\view\meta\PremiumWindowMeta.pyc # decompiled 1 files: 1 okay, 0 failed, 0 verify failed # 2016.11.19 19:51:28 Střední Evropa (běžný čas)

[ "info@webium.sk" ]

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2023-08-15T00:21:52.750270

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# Copyright 2021 Hakan Kjellerstrand hakank@gmail.com # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Futoshiki problem in OR-tools CP-SAT Solver. From http://en.wikipedia.org/wiki/Futoshiki ''' The puzzle is played on a square grid, such as 5 x 5. The objective is to place the numbers 1 to 5 (or whatever the dimensions are) such that each row, and column contains each of the digits 1 to 5. Some digits may be given at the start. In addition, inequality constraints are also initially specifed between some of the squares, such that one must be higher or lower than its neighbour. These constraints must be honoured as the grid is filled out. ''' Also see http://www.guardian.co.uk/world/2006/sep/30/japan.estheraddley This model is inspired by the Minion/Tailor example futoshiki.eprime. It's a port of my old CP model futoshiki.py This model was created by Hakan Kjellerstrand (hakank@gmail.com) Also see my other OR-tools models: http://www.hakank.org/or_tools/ """ from __future__ import print_function from ortools.sat.python import cp_model as cp import math, sys # from cp_sat_utils import * def main(values, lt): model = cp.CpModel() # # data # size = len(values) RANGE = list(range(size)) NUMQD = list(range(len(lt))) # # variables # field = {} for i in RANGE: for j in RANGE: field[i, j] = model.NewIntVar(1, size, "field[%i,%i]" % (i, j)) field_flat = [field[i, j] for i in RANGE for j in RANGE] # # constraints # # set initial values for row in RANGE: for col in RANGE: if values[row][col] > 0: model.Add(field[row, col] == values[row][col]) # all rows have to be different for row in RANGE: model.AddAllDifferent([field[row, col] for col in RANGE]) # all columns have to be different for col in RANGE: model.AddAllDifferent([field[row, col] for row in RANGE]) # all < constraints are satisfied # Also: make 0-based for i in NUMQD: model.Add( field[lt[i][0] - 1, lt[i][1] - 1] < field[lt[i][2] - 1, lt[i][3] - 1]) # # search and result # solver = cp.CpSolver() status = solver.Solve(model) if status == cp.OPTIMAL: for i in RANGE: for j in RANGE: print(solver.Value(field[i, j]), end=" ") print() print() # print("num_solutions:", num_solutions) print("NumConflicts:", solver.NumConflicts()) print("NumBranches:", solver.NumBranches()) print("WallTime:", solver.WallTime()) # # Example from Tailor model futoshiki.param/futoshiki.param # Solution: # 5 1 3 2 4 # 1 4 2 5 3 # 2 3 1 4 5 # 3 5 4 1 2 # 4 2 5 3 1 # # Futoshiki instance, by Andras Salamon # specify the numbers in the grid # values1 = [[0, 0, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]] # [i1,j1, i2,j2] requires that values[i1,j1] < values[i2,j2] # Note: 1-based lt1 = [[1, 2, 1, 1], [1, 4, 1, 5], [2, 3, 1, 3], [3, 3, 2, 3], [3, 4, 2, 4], [2, 5, 3, 5], [3, 2, 4, 2], [4, 4, 4, 3], [5, 2, 5, 1], [5, 4, 5, 3], [5, 5, 4, 5]] # # Example from http://en.wikipedia.org/wiki/Futoshiki # Solution: # 5 4 3 2 1 # 4 3 1 5 2 # 2 1 4 3 5 # 3 5 2 1 4 # 1 2 5 4 3 # values2 = [[0, 0, 0, 0, 0], [4, 0, 0, 0, 2], [0, 0, 4, 0, 0], [0, 0, 0, 0, 4], [0, 0, 0, 0, 0]] # Note: 1-based lt2 = [[1, 2, 1, 1], [1, 4, 1, 3], [1, 5, 1, 4], [4, 4, 4, 5], [5, 1, 5, 2], [5, 2, 5, 3]] if __name__ == "__main__": print("Problem 1") main(values1, lt1) print("\nProblem 2") main(values2, lt2)

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# Copyright (c) Microsoft Corporation. # SPDX-License-Identifier: Apache-2.0 # DeepSpeed Team import torch from torch.nn import functional as F from deepspeed.ops.sparse_attention import BertSparseSelfAttention, SparsityConfig ''' This file contains few utility functions to handle adapting pretrained model with sparse self-attention module. ''' class SparseAttentionUtils: """This class provides some utility functions that are use integrating sparse attention into transformer models. Such utilities include extending position embeddings, replacing current self-attention layer with sparse attention, padding sequences to multiple of block size, etc. """ @staticmethod def extend_position_embedding(model, max_position): """This function extends the position embedding weights of a model loaded from a checkpoint. It assumes the new max position is bigger than the original max length. Arguments: model: required: a transformer model max_position: required: an integer determining new position embedding size Return: model: updated model; in which position embedding weights have been extended based on new size """ if hasattr(model, 'bert'): original_max_position = model.bert.embeddings.position_embeddings.weight.size(0) assert max_position > original_max_position extend_multiples = max(1, max_position // original_max_position) model.bert.embeddings.position_embeddings.weight.data = model.bert.embeddings.position_embeddings.weight.repeat( extend_multiples, 1) elif hasattr(model, 'roberta'): # RoBERTa has positions 0 & 1 reserved, so embedding size is max position + 2 original_max_position, embed_size = model.roberta.embeddings.position_embeddings.weight.shape original_max_position -= 2 extend_multiples = max(1, max_position // original_max_position) assert max_position > original_max_position max_position += 2 extended_position_embedding = model.roberta.embeddings.position_embeddings.weight.new_empty( max_position, embed_size) k = 2 for i in range(extend_multiples): extended_position_embedding[k:( k + original_max_position)] = model.roberta.embeddings.position_embeddings.weight[2:] k += original_max_position model.roberta.embeddings.position_embeddings.weight.data = extended_position_embedding else: raise ValueError( 'Please extend \"extend_position_embedding\" function to support your model type. It currently only supports \"bert\" & \"roberta\"!' ) model.config.max_position_embeddings = max_position print(f'Extended position embeddings to {original_max_position * extend_multiples}') return model @staticmethod def update_tokenizer_model_max_length(tokenizer, max_position): """This function updates the position embedding length of a tokenizer to a new max position. Arguments: tokenizer: required: a transformer tokenizer max_position: required: an integer determining new position embedding size Return: tokenizer: updated tokenizer; in which model maximum length has been extended based on new size """ tokenizer.model_max_length = max_position tokenizer.init_kwargs['model_max_length'] = max_position print(f'updated tokenizer model max imum length to {max_position}') return tokenizer @staticmethod def replace_model_self_attention_with_sparse_self_attention( model, max_position, # SparsityConfig parameters needs to be set accordingly sparsity_config=SparsityConfig(num_heads=4)): """This function replaces the self attention layers in model encoder with sparse self attention. It currently supports bert and roberta model and can be easily extended to any other models following similar steps here. For sparsityConfig, refer to the config class. Arguments: model: required: a transformer model max_position: required: an integer determining new position embedding size sparsity_config: optional: this parameter determines sparsity pattern configuration; it is based on SparsityConfig class Return: model: updated model; in which self attention layer has been replaced with DeepSpeed Sparse Self Attention layer. """ if hasattr(model, 'bert'): model.config.max_position_embeddings = max_position model.replace_self_attention_layer_with_sparse_self_attention_layer(model.config, model.bert.encoder.layer, sparsity_config) elif hasattr(model, 'roberta'): model.config.max_position_embeddings = max_position + 2 model.replace_self_attention_layer_with_sparse_self_attention_layer(model.config, model.roberta.encoder.layer, sparsity_config) else: raise ValueError( 'Please extend \"update_model_self_attention_to_sparse_self_attention\" function to support \ your model type. It currently only supports \"bert\" & \"roberta\"!') return model @staticmethod def replace_self_attention_layer_with_sparse_self_attention_layer( config, layers, # SparsityConfig parameters needs to be set accordingly sparsity_config=SparsityConfig(num_heads=4)): """This function replaces the self attention layers in attention layer with sparse self attention. For sparsityConfig, refer to the config class. Arguments: config: required: transformer model config layers: required: transformer model attention layers sparsity_config: optional: this parameter determines sparsity pattern configuration; it is based on SparsityConfig class Return: layers: updated attention layers; in which self attention layers have been replaced with DeepSpeed Sparse Self Attention layer. """ for layer in layers: deepspeed_sparse_self_attn = BertSparseSelfAttention(config, sparsity_config) deepspeed_sparse_self_attn.query = layer.attention.self.query deepspeed_sparse_self_attn.key = layer.attention.self.key deepspeed_sparse_self_attn.value = layer.attention.self.value layer.attention.self = deepspeed_sparse_self_attn return layers @staticmethod def pad_to_block_size(block_size, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds, pad_token_id, model_embeddings): """This function pads input tokens and attention mask on sequence length dimension to be multiple of block size. This is a requirement for Sparse Transformer in which the self attention layer works on sequences of length multiple of block size. It needs to be called in your model, such as BertModel, right before you calculate the embedding outputs. Note) 1- instead of passing your embedding layer to this function, you can simply add this function to your model. It can be more simplified if given attention_mask and/or token_type_ids are none. 2- you need to call unpad function before returning your model output to unpad the encoder sequence output. Arguments: block_size: required: an integer determining the block size of sparsity config. pad_token_id: required: an integer determining the pad token from the model config; such as bert.config.pad_token_id. input_ids: a torch.LongTensor of shape [batch_size, sequence_length] with the word token indices in the vocabulary attention_mask: a torch.LongTensor of shape [batch_size, sequence_length] with indices selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max input sequence length in the current batch. It's the mask that we typically use for attention when a batch has varying length sentences. token_type_ids: a torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to a `sentence B` token (see BERT paper for more details). position_ids: a torch.LongTensor of shape [batch_size, sequence_length] with the indices of positions of each input sequence tokens in the position embeddings. inputs_embeds: an optional torch.FloatTensor of shape [batch_size, sequence_length, hidden_size] that contains embedded representation and can be passed instead of input_ids directly. model_embeddings: an optional object. If inputs_embeds are not none, this will be your model embeddings such as BertEmbeddings from your model such as BertModel. You can move this function inside your model and use self.embeddings instead of passing this parameter. Return: pad_len: an integer determining how much inputs have been padded to transfer sequence length dimension to multiple of block size. input_ids: if input_ids are not none padded input_ids otherwise none. attention_mask: if attention_mask is not none padded attention_mask otherwise none. token_type_ids: if token_type_ids are not none padded token_type_ids otherwise none. position_ids: if position_ids are not none padded position_ids otherwise none. inputs_embeds: if inputs_embeds are not none padded inputs_embeds otherwise none. """ batch_size, seq_len = input_ids.shape if input_ids is not None else inputs_embeds.shape[:-1] pad_len = (block_size - seq_len % block_size) % block_size if pad_len > 0: if inputs_embeds is not None: pad_input_ids = inputs_embeds.new_full((batch_size, pad_len), pad_token_id, dtype=torch.long) pad_inputs_embeds = model_embeddings(pad_input_ids) inputs_embeds = torch.cat([inputs_embeds, pad_inputs_embeds], dim=-2) # may not be needed as input_ids are not used if inputs_embeds are given if input_ids is not None: input_ids = F.pad(input_ids, (0, pad_len), value=pad_token_id) if position_ids is not None: # pad position_id with pad_token_id position_ids = F.pad(position_ids, (0, pad_len), value=pad_token_id) # pad attention mask without attention on the padding tokens attention_mask = F.pad(attention_mask, (0, pad_len), value=False) # pad token_type_ids with token_type_id = 0 token_type_ids = F.pad(token_type_ids, (0, pad_len), value=0) return pad_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds @staticmethod def unpad_sequence_output(pad_len, sequence_output): """This function unpads sequence output if inputs of the model were padded. This is a requirement for Sparse Transformer in which the self attention layer works on sequences of length multiple of block size. It needs to be called in your model, such as BertModel, right before you return the model outputs. Arguments: pad_len: required: an integer determining how much model inputs have been padded to transfer sequence length dimension to multiple of block size. sequence_output: required: sequence output of the encoder layer. Return: sequence_output: unpaded sequence output of the encoder layer. """ if (pad_len > 0): sequence_output = sequence_output[:, :-pad_len] return sequence_output

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import numpy as np from apal import Khachaturyan import matplotlib as mpl mpl.rcParams.update({'font.size': 18, 'axes.unicode_minus': False, 'svg.fonttype': 'none'}) from matplotlib import pyplot as plt C_al = np.array([[0.62639459, 0.41086487, 0.41086487, 0, 0, 0], [0.41086487, 0.62639459, 0.41086487, 0, 0, 0], [0.41086487, 0.41086487, 0.62639459, 0, 0, 0], [0, 0, 0, 0.42750351, 0, 0], [0, 0, 0, 0, 0.42750351, 0], [0, 0, 0, 0, 0, 0.42750351]]) SIZE = 512 MISFIT = np.array([[0.0440222, 0.00029263, 0.0008603], [0.00029263, -0.0281846, 0.00029263], [0.0008603, 0.00029263, 0.0440222]]) def strain_energy(radius, length): from cylinder import create_cylinder khach = Khachaturyan(elastic_tensor=C_al, misfit_strain=MISFIT) voxels = np.zeros((SIZE, SIZE, SIZE), dtype=np.int32) voxels = create_cylinder(voxels, radius, length, SIZE) print("Created cylinder") energy = khach.strain_energy_voxels(voxels) print("Strain energy: {} meV/A^3".format(energy*1000)) return energy*1000.0 def strain_ellipsoid(a, b, c): from cylinder import create_ellipsoid khach = Khachaturyan(elastic_tensor=C_al, misfit_strain=MISFIT) voxels = np.zeros((SIZE, SIZE, SIZE), dtype=np.int32) voxels = create_ellipsoid(voxels, a, b, c, SIZE) print("Created ellipsoid") energy = khach.strain_energy_voxels(voxels) print("Strain energy: {} meV/A^3 (a={},b={},c={})".format(energy*1000, a, b, c)) return energy*1000.0 def calculate_all(): r = 20 data = [] for d in range(2, 200, 4): energy = strain_energy(r, d) data.append([r, d, energy]) fname = "data/strain_energy_cylinder{}.csv".format(int(r)) np.savetxt(fname, data, delimiter=",", header="Radius (A), Length (A), Energy (meV/A^3)") def calculate_ellipsoid(): a = c = 20 data = [] flip_ba = True for b in list(range(2, 20, 4)) + list(range(20, 200, 20)): if flip_ba: energy = strain_ellipsoid(b, a, c) else: energy = strain_ellipsoid(a, b, c) data.append([a, b, c, energy]) if flip_ba: fname = "data/strain_energy_ellipsoid{}_flipped.csv".format(int(a)) else: fname = "data/strain_energy_ellipsoid{}.csv".format(int(a)) np.savetxt(fname, data, delimiter=",", header="Half-axis x (A), Half-axis y (A), Half-axis z (A), Energy (meV/A^3)") def save_voxels(radius, length): from cylinder import create_cylinder voxels = np.zeros((SIZE, SIZE, SIZE), dtype=np.int32) voxels = create_cylinder(voxels, radius, length, SIZE) voxels = np.array(voxels, dtype=np.uint8) fname = "/work/sophus/cylinder_R{}_L{}.bin".format(int(radius), int(length)) voxels.tofile(fname) print("Voxels written to {}".format(fname)) def plot_strain_energy(fname): data = np.loadtxt(fname, delimiter=",") aspect = data[:, 1]/data[:, 0] energy = data[:, 2] fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.plot(aspect, energy, color="#5d5c61") ax.set_xlabel("Aspect ratio (L/R)") ax.set_ylabel(r"Strain energy (meV/\r{A}\$^3\$)") ax.spines["right"].set_visible(False) ax.spines["top"].set_visible(False) plt.show() def plot_strain_energy_ellipsoids(): data = np.loadtxt("data/strain_energy_ellipsoid20.csv", delimiter=",") data_flipped = np.loadtxt("data/strain_energy_ellipsoid20_flipped.csv", delimiter=",") aspect = data[:, 1]/data[:, 0] aspect_flipped = data_flipped[:, 1]/data_flipped[:, 0] energy = data[:, 3] energy_flipped = data_flipped[:, 3] fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.plot(aspect, energy, color="#5d5c61", marker="o", mfc="none") ax.plot(aspect_flipped, energy_flipped, color="#557a95", marker="v", mfc="none") ax.set_xlabel("Aspect ratio (L/R)") ax.set_ylabel(r"Strain energy (meV/\r{A}\$^3\$)") ax.spines["right"].set_visible(False) ax.spines["top"].set_visible(False) plt.show() #calculate_all() #calculate_ellipsoid() plot_strain_energy_ellipsoids() #plot_strain_energy("data/strain_energy_cylinder20.csv") #save_voxels(50, 400)

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#! /usr/bin/env python # -*- coding: utf-8 -*- # vim: set ts=4 sts=4 sw=4 et: ## @brief 根据条件检索function。current_page:最大100页 page_size:10-30 # @author wuliang@maimiaotech.com # @date 2013-09-22 16:52:39 # @version: 0.0.0 import os import sys import time def __getCurrentPath(): return os.path.normpath(os.path.join(os.path.realpath(__file__), os.path.pardir)) __modulePath = os.path.join(__getCurrentPath(), os.path.pardir) __modulePath = os.path.normpath(__modulePath) if __modulePath not in sys.path: sys.path.insert(0, __modulePath) ## @brief <SPAN style="font-size:16px; font-family:'宋体','Times New Roman',Georgia,Serif;">根据条件检索function。current_page:最大100页 page_size:10-30</SPAN> # <UL> # </UL> class HanoiFunctionSearchRequest(object): def __init__(self): super(self.__class__, self).__init__() ## @brief <SPAN style="font-size:16px; font-family:'宋体','Times New Roman',Georgia,Serif;">获取API名称</SPAN> # <UL> # <LI> # <SPAN style="color:DarkRed; font-size:18px; font-family:'Times New Roman',Georgia,Serif;">Type</SPAN>: <SPAN style="color:DarkMagenta; font-size:16px; font-family:'Times New Roman','宋体',Georgia,Serif;">str</SPAN> # </LI> # </UL> self.method = "taobao.hanoi.function.search" ## @brief <SPAN style="font-size:16px; font-family:'宋体','Times New Roman',Georgia,Serif;">时间戳，如果不设置,发送请求时将使用当时的时间</SPAN> # <UL> # <LI> # <SPAN style="color:DarkRed; font-size:18px; font-family:'Times New Roman',Georgia,Serif;">Type</SPAN>: <SPAN style="color:DarkMagenta; font-size:16px; font-family:'Times New Roman','宋体',Georgia,Serif;">int</SPAN> # </LI> # </UL> self.timestamp = int(time.time()) ## @brief <SPAN style="font-size:16px; font-family:'宋体','Times New Roman',Georgia,Serif;">分配给调用方的名称信息，内部统计使用</SPAN> # <UL> # <LI> # <SPAN style="color:DarkRed; font-size:18px; font-family:'Times New Roman',Georgia,Serif;">Type</SPAN>: <SPAN style="color:DarkMagenta; font-size:16px; font-family:'Times New Roman','宋体',Georgia,Serif;">String</SPAN> # </LI> # <LI> # <SPAN style="color:DarkRed; font-size:18px; font-family:'Times New Roman',Georgia,Serif;">Required</SPAN>: <SPAN style="color:DarkMagenta; font-size:16px; font-family:'Times New Roman','宋体',Georgia,Serif;">required</SPAN> # </LI> # </UL> self.app_name = None ## @brief <SPAN style="font-size:16px; font-family:'宋体','Times New Roman',Georgia,Serif;">FunctionQuery的json格式</SPAN> # <UL> # <LI> # <SPAN style="color:DarkRed; font-size:18px; font-family:'Times New Roman',Georgia,Serif;">Type</SPAN>: <SPAN style="color:DarkMagenta; font-size:16px; font-family:'Times New Roman','宋体',Georgia,Serif;">String</SPAN> # </LI> # <LI> # <SPAN style="color:DarkRed; font-size:18px; font-family:'Times New Roman',Georgia,Serif;">Required</SPAN>: <SPAN style="color:DarkMagenta; font-size:16px; font-family:'Times New Roman','宋体',Georgia,Serif;">required</SPAN> # </LI> # </UL> self.sdata = None

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""" Tests dtype specification during parsing for all of the parsers defined in parsers.py """ from collections import defaultdict from io import StringIO import numpy as np import pytest from pandas.errors import ParserWarning import pandas as pd from pandas import ( DataFrame, Timestamp, ) import pandas._testing as tm from pandas.core.arrays import ( ArrowStringArray, IntegerArray, StringArray, ) @pytest.mark.parametrize("dtype", [str, object]) @pytest.mark.parametrize("check_orig", [True, False]) @pytest.mark.usefixtures("pyarrow_xfail") def test_dtype_all_columns(all_parsers, dtype, check_orig): # see gh-3795, gh-6607 parser = all_parsers df = DataFrame( np.random.rand(5, 2).round(4), columns=list("AB"), index=["1A", "1B", "1C", "1D", "1E"], ) with tm.ensure_clean("__passing_str_as_dtype__.csv") as path: df.to_csv(path) result = parser.read_csv(path, dtype=dtype, index_col=0) if check_orig: expected = df.copy() result = result.astype(float) else: expected = df.astype(str) tm.assert_frame_equal(result, expected) @pytest.mark.usefixtures("pyarrow_xfail") def test_dtype_per_column(all_parsers): parser = all_parsers data = """\ one,two 1,2.5 2,3.5 3,4.5 4,5.5""" expected = DataFrame( [[1, "2.5"], [2, "3.5"], [3, "4.5"], [4, "5.5"]], columns=["one", "two"] ) expected["one"] = expected["one"].astype(np.float64) expected["two"] = expected["two"].astype(object) result = parser.read_csv(StringIO(data), dtype={"one": np.float64, 1: str}) tm.assert_frame_equal(result, expected) @pytest.mark.usefixtures("pyarrow_xfail") def test_invalid_dtype_per_column(all_parsers): parser = all_parsers data = """\ one,two 1,2.5 2,3.5 3,4.5 4,5.5""" with pytest.raises(TypeError, match="data type [\"']foo[\"'] not understood"): parser.read_csv(StringIO(data), dtype={"one": "foo", 1: "int"}) @pytest.mark.usefixtures("pyarrow_xfail") def test_raise_on_passed_int_dtype_with_nas(all_parsers): # see gh-2631 parser = all_parsers data = """YEAR, DOY, a 2001,106380451,10 2001,,11 2001,106380451,67""" msg = ( "Integer column has NA values" if parser.engine == "c" else "Unable to convert column DOY" ) with pytest.raises(ValueError, match=msg): parser.read_csv(StringIO(data), dtype={"DOY": np.int64}, skipinitialspace=True) @pytest.mark.usefixtures("pyarrow_xfail") def test_dtype_with_converters(all_parsers): parser = all_parsers data = """a,b 1.1,2.2 1.2,2.3""" # Dtype spec ignored if converted specified. result = parser.read_csv_check_warnings( ParserWarning, "Both a converter and dtype were specified for column a " "- only the converter will be used.", StringIO(data), dtype={"a": "i8"}, converters={"a": lambda x: str(x)}, ) expected = DataFrame({"a": ["1.1", "1.2"], "b": [2.2, 2.3]}) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "dtype", list(np.typecodes["AllInteger"] + np.typecodes["Float"]) ) def test_numeric_dtype(all_parsers, dtype): data = "0\n1" parser = all_parsers expected = DataFrame([0, 1], dtype=dtype) result = parser.read_csv(StringIO(data), header=None, dtype=dtype) tm.assert_frame_equal(expected, result) @pytest.mark.usefixtures("pyarrow_xfail") def test_boolean_dtype(all_parsers): parser = all_parsers data = "\n".join( [ "a", "True", "TRUE", "true", "1", "1.0", "False", "FALSE", "false", "0", "0.0", "NaN", "nan", "NA", "null", "NULL", ] ) result = parser.read_csv(StringIO(data), dtype="boolean") expected = DataFrame( { "a": pd.array( [ True, True, True, True, True, False, False, False, False, False, None, None, None, None, None, ], dtype="boolean", ) } ) tm.assert_frame_equal(result, expected) @pytest.mark.usefixtures("pyarrow_xfail") def test_delimiter_with_usecols_and_parse_dates(all_parsers): # GH#35873 result = all_parsers.read_csv( StringIO('"dump","-9,1","-9,1",20101010'), engine="python", names=["col", "col1", "col2", "col3"], usecols=["col1", "col2", "col3"], parse_dates=["col3"], decimal=",", ) expected = DataFrame( {"col1": [-9.1], "col2": [-9.1], "col3": [Timestamp("2010-10-10")]} ) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("thousands", ["_", None]) def test_decimal_and_exponential( request, python_parser_only, numeric_decimal, thousands ): # GH#31920 decimal_number_check(request, python_parser_only, numeric_decimal, thousands, None) @pytest.mark.parametrize("thousands", ["_", None]) @pytest.mark.parametrize("float_precision", [None, "legacy", "high", "round_trip"]) def test_1000_sep_decimal_float_precision( request, c_parser_only, numeric_decimal, float_precision, thousands ): # test decimal and thousand sep handling in across 'float_precision' # parsers decimal_number_check( request, c_parser_only, numeric_decimal, thousands, float_precision ) text, value = numeric_decimal text = " " + text + " " if isinstance(value, str): # the negative cases (parse as text) value = " " + value + " " decimal_number_check( request, c_parser_only, (text, value), thousands, float_precision ) def decimal_number_check(request, parser, numeric_decimal, thousands, float_precision): # GH#31920 value = numeric_decimal[0] if thousands is None and value in ("1_,", "1_234,56", "1_234,56e0"): request.node.add_marker( pytest.mark.xfail(reason=f"thousands={thousands} and sep is in {value}") ) df = parser.read_csv( StringIO(value), float_precision=float_precision, sep="|", thousands=thousands, decimal=",", header=None, ) val = df.iloc[0, 0] assert val == numeric_decimal[1] @pytest.mark.parametrize("float_precision", [None, "legacy", "high", "round_trip"]) def test_skip_whitespace(c_parser_only, float_precision): DATA = """id\tnum\t 1\t1.2 \t 1\t 2.1\t 2\t 1\t 2\t 1.2 \t """ df = c_parser_only.read_csv( StringIO(DATA), float_precision=float_precision, sep="\t", header=0, dtype={1: np.float64}, ) tm.assert_series_equal(df.iloc[:, 1], pd.Series([1.2, 2.1, 1.0, 1.2], name="num")) @pytest.mark.usefixtures("pyarrow_xfail") def test_true_values_cast_to_bool(all_parsers): # GH#34655 text = """a,b yes,xxx no,yyy 1,zzz 0,aaa """ parser = all_parsers result = parser.read_csv( StringIO(text), true_values=["yes"], false_values=["no"], dtype={"a": "boolean"}, ) expected = DataFrame( {"a": [True, False, True, False], "b": ["xxx", "yyy", "zzz", "aaa"]} ) expected["a"] = expected["a"].astype("boolean") tm.assert_frame_equal(result, expected) @pytest.mark.usefixtures("pyarrow_xfail") @pytest.mark.parametrize("dtypes, exp_value", [({}, "1"), ({"a.1": "int64"}, 1)]) def test_dtype_mangle_dup_cols(all_parsers, dtypes, exp_value): # GH#35211 parser = all_parsers data = """a,a\n1,1""" dtype_dict = {"a": str, **dtypes} # GH#42462 dtype_dict_copy = dtype_dict.copy() result = parser.read_csv(StringIO(data), dtype=dtype_dict) expected = DataFrame({"a": ["1"], "a.1": [exp_value]}) assert dtype_dict == dtype_dict_copy, "dtype dict changed" tm.assert_frame_equal(result, expected) @pytest.mark.usefixtures("pyarrow_xfail") def test_dtype_mangle_dup_cols_single_dtype(all_parsers): # GH#42022 parser = all_parsers data = """a,a\n1,1""" result = parser.read_csv(StringIO(data), dtype=str) expected = DataFrame({"a": ["1"], "a.1": ["1"]}) tm.assert_frame_equal(result, expected) @pytest.mark.usefixtures("pyarrow_xfail") def test_dtype_multi_index(all_parsers): # GH 42446 parser = all_parsers data = "A,B,B\nX,Y,Z\n1,2,3" result = parser.read_csv( StringIO(data), header=list(range(2)), dtype={ ("A", "X"): np.int32, ("B", "Y"): np.int32, ("B", "Z"): np.float32, }, ) expected = DataFrame( { ("A", "X"): np.int32([1]), ("B", "Y"): np.int32([2]), ("B", "Z"): np.float32([3]), } ) tm.assert_frame_equal(result, expected) def test_nullable_int_dtype(all_parsers, any_int_ea_dtype): # GH 25472 parser = all_parsers dtype = any_int_ea_dtype data = """a,b,c ,3,5 1,,6 2,4,""" expected = DataFrame( { "a": pd.array([pd.NA, 1, 2], dtype=dtype), "b": pd.array([3, pd.NA, 4], dtype=dtype), "c": pd.array([5, 6, pd.NA], dtype=dtype), } ) actual = parser.read_csv(StringIO(data), dtype=dtype) tm.assert_frame_equal(actual, expected) @pytest.mark.usefixtures("pyarrow_xfail") @pytest.mark.parametrize("default", ["float", "float64"]) def test_dtypes_defaultdict(all_parsers, default): # GH#41574 data = """a,b 1,2 """ dtype = defaultdict(lambda: default, a="int64") parser = all_parsers result = parser.read_csv(StringIO(data), dtype=dtype) expected = DataFrame({"a": [1], "b": 2.0}) tm.assert_frame_equal(result, expected) @pytest.mark.usefixtures("pyarrow_xfail") def test_dtypes_defaultdict_mangle_dup_cols(all_parsers): # GH#41574 data = """a,b,a,b,b.1 1,2,3,4,5 """ dtype = defaultdict(lambda: "float64", a="int64") dtype["b.1"] = "int64" parser = all_parsers result = parser.read_csv(StringIO(data), dtype=dtype) expected = DataFrame({"a": [1], "b": [2.0], "a.1": [3], "b.2": [4.0], "b.1": [5]}) tm.assert_frame_equal(result, expected) @pytest.mark.usefixtures("pyarrow_xfail") def test_dtypes_defaultdict_invalid(all_parsers): # GH#41574 data = """a,b 1,2 """ dtype = defaultdict(lambda: "invalid_dtype", a="int64") parser = all_parsers with pytest.raises(TypeError, match="not understood"): parser.read_csv(StringIO(data), dtype=dtype) def test_dtype_backend(all_parsers): # GH#36712 parser = all_parsers data = """a,b,c,d,e,f,g,h,i,j 1,2.5,True,a,,,,,12-31-2019, 3,4.5,False,b,6,7.5,True,a,12-31-2019, """ result = parser.read_csv( StringIO(data), dtype_backend="numpy_nullable", parse_dates=["i"] ) expected = DataFrame( { "a": pd.Series([1, 3], dtype="Int64"), "b": pd.Series([2.5, 4.5], dtype="Float64"), "c": pd.Series([True, False], dtype="boolean"), "d": pd.Series(["a", "b"], dtype="string"), "e": pd.Series([pd.NA, 6], dtype="Int64"), "f": pd.Series([pd.NA, 7.5], dtype="Float64"), "g": pd.Series([pd.NA, True], dtype="boolean"), "h": pd.Series([pd.NA, "a"], dtype="string"), "i": pd.Series([Timestamp("2019-12-31")] * 2), "j": pd.Series([pd.NA, pd.NA], dtype="Int64"), } ) tm.assert_frame_equal(result, expected) def test_dtype_backend_and_dtype(all_parsers): # GH#36712 parser = all_parsers data = """a,b 1,2.5 , """ result = parser.read_csv( StringIO(data), dtype_backend="numpy_nullable", dtype="float64" ) expected = DataFrame({"a": [1.0, np.nan], "b": [2.5, np.nan]}) tm.assert_frame_equal(result, expected) def test_dtype_backend_string(all_parsers, string_storage): # GH#36712 pa = pytest.importorskip("pyarrow") with pd.option_context("mode.string_storage", string_storage): parser = all_parsers data = """a,b a,x b, """ result = parser.read_csv(StringIO(data), dtype_backend="numpy_nullable") if string_storage == "python": expected = DataFrame( { "a": StringArray(np.array(["a", "b"], dtype=np.object_)), "b": StringArray(np.array(["x", pd.NA], dtype=np.object_)), } ) else: expected = DataFrame( { "a": ArrowStringArray(pa.array(["a", "b"])), "b": ArrowStringArray(pa.array(["x", None])), } ) tm.assert_frame_equal(result, expected) def test_dtype_backend_ea_dtype_specified(all_parsers): # GH#491496 data = """a,b 1,2 """ parser = all_parsers result = parser.read_csv( StringIO(data), dtype="Int64", dtype_backend="numpy_nullable" ) expected = DataFrame({"a": [1], "b": 2}, dtype="Int64") tm.assert_frame_equal(result, expected) def test_dtype_backend_pyarrow(all_parsers, request): # GH#36712 pa = pytest.importorskip("pyarrow") parser = all_parsers data = """a,b,c,d,e,f,g,h,i,j 1,2.5,True,a,,,,,12-31-2019, 3,4.5,False,b,6,7.5,True,a,12-31-2019, """ result = parser.read_csv(StringIO(data), dtype_backend="pyarrow", parse_dates=["i"]) expected = DataFrame( { "a": pd.Series([1, 3], dtype="int64[pyarrow]"), "b": pd.Series([2.5, 4.5], dtype="float64[pyarrow]"), "c": pd.Series([True, False], dtype="bool[pyarrow]"), "d": pd.Series(["a", "b"], dtype=pd.ArrowDtype(pa.string())), "e": pd.Series([pd.NA, 6], dtype="int64[pyarrow]"), "f": pd.Series([pd.NA, 7.5], dtype="float64[pyarrow]"), "g": pd.Series([pd.NA, True], dtype="bool[pyarrow]"), "h": pd.Series( [pd.NA, "a"], dtype=pd.ArrowDtype(pa.string()), ), "i": pd.Series([Timestamp("2019-12-31")] * 2), "j": pd.Series([pd.NA, pd.NA], dtype="null[pyarrow]"), } ) tm.assert_frame_equal(result, expected) def test_ea_int_avoid_overflow(all_parsers): # GH#32134 parser = all_parsers data = """a,b 1,1 ,1 1582218195625938945,1 """ result = parser.read_csv(StringIO(data), dtype={"a": "Int64"}) expected = DataFrame( { "a": IntegerArray( np.array([1, 1, 1582218195625938945]), np.array([False, True, False]) ), "b": 1, } ) tm.assert_frame_equal(result, expected)

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import json import logging from flask import request from werkzeug.exceptions import abort from ooi_instrument_agent.client import ZmqDriverClient DEFAULT_TIMEOUT = 90000 log = logging.getLogger(__name__) def get_client(consul, driver_id): """ Create a ZmqDriverClient for the specified driver_id :param consul: Instance of consul.Consul :param driver_id: Reference designator of target driver :return: ZmqDriverClient if found, otherwise 404 """ return ZmqDriverClient(*get_host_and_port(consul, driver_id)) def get_host_and_port(consul, driver_id): """ Return the host and port for the specified driver_id :param consul: Instance of consul.Consul :param driver_id: Reference designator of target driver :return: host, port if found, otherwise 404 """ host_and_port = get_service_host_and_port(consul, 'instrument_driver', tag=driver_id) if host_and_port is None: abort(404) return host_and_port def get_service_host_and_port(consul, service_id, tag=None): """ Return the first passing host and port for the specified service_id :param consul: Instance of consul.Consul :param service_id: service_id :param tag: tag :return: host, port if found, otherwise None """ index, matches = consul.health.service(service_id, tag=tag, passing=True) for match in matches: host = match.get('Node', {}).get('Address') port = match.get('Service', {}).get('Port') if host and port: return host, port def list_drivers(consul): """ Return a list of all passing drivers currently registered in Consul :param consul: Instance of consul.Consul :return: List of reference designators """ drivers = [] index, passing = consul.health.service('instrument_driver', passing=True) for each in passing: tags = each.get('Service', {}).get('Tags', []) drivers.extend(tags) return drivers def get_port_agent(consul, driver_id): """ Fetch the port agent information for the specified driver from Consul :param consul: Instance of consul.Consul :param driver_id: Reference designator of target driver :return: Dictionary containing the port agent data for the specified driver """ return_dict = {} for name, service_id in [('data', 'port-agent'), ('command', 'command-port-agent'), ('sniff', 'sniff-port-agent'), ('da', 'da-port-agent')]: host_and_port = get_service_host_and_port(consul, service_id, tag=driver_id) if host_and_port: host, port = host_and_port return_dict[name] = {'host': host, 'port': port} if return_dict: return return_dict abort(404) def get_from_request(name, default=None): """ Extract the target parameter from a Flask request object. Attempts to do the right thing whether the input data was passed as URL query params, a form or as JSON. :param name: Target parameter :param default: Default value to return if not found :return: Extracted value if found, else default """ def extract(value_dict, name): val = value_dict.get(name) if val is None: return default try: val = json.loads(val) except (TypeError, ValueError): pass return val if request.args: return extract(request.args, name) if request.form: return extract(request.form, name) if request.json: return request.json.get(name, default) return default def get_timeout(): """ Get the timeout from the request object as an int :return: timeout """ val = get_from_request('timeout') try: return int(val) except (ValueError, TypeError): return DEFAULT_TIMEOUT

[ "petercable@gmail.com" ]

petercable@gmail.com

1a3dcd0ed91952cbd51126d875f0e262109a6f94

6244c2efe590494e0870253269e269848f8debe4

/BooleanNetworks/LEMScores/parseLEMscores_malaria_40hr.py

272717bc1a9856b72ebf33574f87bff521033bb8

[]

no_license

breecummins/BooleanNetworks

53db4dc4e50d5d571344ed55b65efb66a1c4328d

074409a6dd569b2f0ce3602e7dfda496db08cd01

refs/heads/master

2021-09-03T20:31:53.237492

2018-01-11T19:55:32

117,146,650

0

null

UTF-8

Python

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4,021

py

import parseLEMscores_yeast_mouse as PLS import parseLEMscores_malaria_20hr as PLS20 from networkbuilder_yeast_mouse import createNetworkFile import time def parseLEMfile(bound=0,fname='/Users/bcummins/ProjectData/malaria/wrair2015_v2_fpkm-p1_s19_40hr_highest_ranked_genes/wrair2015_v2_fpkm-p1_s19_90tfs_top25_dljtk_lem_score_table.txt'): # returns the source, target, and type of regulation sorted by decreasing LEM score (also returned) source=[] type_reg=[] target=[] lem_score=[] with open(fname,'r') as f: for _ in range(8): f.readline() for l in f.readlines(): wordlist=l.split() lem = float(wordlist[5]) if lem>bound: target.append(wordlist[0]) lem_score.append(lem) two_words=wordlist[2].split('(') type_reg.append(two_words[0]) source.append(two_words[1][:-1]) [lem_score,source,target,type_reg] = PLS.sort_by_list_in_reverse(lem_score,[source,target,type_reg]) return source,target,type_reg,lem_score def generateResult(threshold=0.1,frontname='malaria40hr_90TF_top25',makegraph=1,saveme=1,onlylargestnetwork=0,LEMfile='/Users/bcummins/ProjectData/malaria/wrair2015_v2_fpkm-p1_s19_40hr_highest_ranked_genes/wrair2015_v2_fpkm-p1_s19_90tfs_top25_dljtk_lem_score_table.txt',new_network_path='',new_network_date='',essential=True): print 'Parsing file...' source,target,type_reg,lem_score=parseLEMfile(threshold,LEMfile) genes = sorted(set(source).intersection(target)) # print genes print 'Making outedges...' outedges,regulation,LEM_scores=PLS20.makeOutedges(genes,source,target,type_reg,lem_score) # print outedges print 'Extracting strongly connected components...' grouped_scc_gene_inds=PLS20.strongConnectIndices(outedges) scc_genenames=[[genes[g] for g in G] for G in grouped_scc_gene_inds ] # print scc_genes if onlylargestnetwork: L = [len(g) for g in grouped_scc_gene_inds] ind=L.index(max(L)) grouped_scc_gene_inds = grouped_scc_gene_inds[ind] flat_scc_gene_inds = grouped_scc_gene_inds[:] scc_genenames = scc_genenames[ind] flat_scc_genenames = scc_genenames[:] else: flat_scc_gene_inds= [g for G in grouped_scc_gene_inds for g in G] flat_scc_genenames = [s for S in scc_genenames for s in S] outedges,regulation,LEM_scores=PLS20.pruneOutedges(flat_scc_gene_inds,outedges,regulation,LEM_scores) if makegraph: print 'Making graph for {} nodes and {} edges....'.format(len(flat_scc_gene_inds),len([o for oe in outedges for o in oe])) PLS.makeGraph(flat_scc_genenames,outedges,regulation,name='{}_graph_thresh{}.pdf'.format(frontname,str(threshold).replace('.','-'))) if saveme: createNetworkFile(flat_scc_genenames,outedges,regulation,new_network_path+'{}D_'.format(len(flat_scc_genenames))+time.strftime("%Y_%m_%d")+'_{}_T{}'.format(frontname,str(threshold).replace('.','-')) + '_essential'*essential +'.txt',[essential]*len(flat_scc_genenames)) if __name__ == "__main__": # frontname='malaria40hr_90TF_top25' # new_network_path = '/Users/bcummins/GIT/DSGRN/networks/' # LEMfile='/Users/bcummins/ProjectData/malaria/wrair2015_v2_fpkm-p1_s19_40hr_highest_ranked_genes/wrair2015_v2_fpkm-p1_s19_90tfs_top25_dljtk_lem_score_table.txt' # for threshold in [0.01, 0.0075, 0.005, 0.001]: # generateResult(threshold,frontname,1,1,1,LEMfile,new_network_path,True) frontname='malaria40hr_50TF_top25' new_network_path = '/Users/bcummins/GIT/DSGRN/networks/' LEMfile='/Users/bcummins/ProjectData/malaria/wrair2015_v2_fpkm-p1_s19_40hr_highest_ranked_genes/wrair2015_v2_fpkm-p1_s19_50tfs_top25_dljtk_lem_score_table.txt' makegraph=1 saveme=0 onlylargestnetwork=0 essential=True for threshold in [0.02]: generateResult(threshold,frontname,makegraph,saveme,onlylargestnetwork,LEMfile,new_network_path,essential)

[ "breecummins@gmail.com" ]

breecummins@gmail.com

9a670955cc54404b943dfc93a7b7692e7f24ee44

00b5ad360284adc06f7e7ca9b2d1c2d3a0edd6f9

/recycle/CRF-C-LR.py

5571d01c9c38fafa50e8533efab0bdcfe00946ba

[]

no_license

ShenDezhou/CBLSTM

e09d36f609df2b34ace2ae8085d2232039838675

b5ac4714f8ea14cf2bfd6ce6033eb697ef078686

refs/heads/master

2021-04-16T19:47:44.758194

2020-07-20T06:21:08

249,381,106

2

0

null

UTF-8

Python

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2,201

py

#!/usr/bin/env python # -*- coding: utf-8 -*- ''' Created on 2019年5月17日 @author: Administrator ''' from sklearn.feature_extraction.text import CountVectorizer import os import codecs from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_split import pkuseg class Sentiment(object): vectorizer=None log_model=None acc_score=None def __init__(self): pass @classmethod def load_model(cls_obj): data = [] data_labels = [] for filename in os.listdir(u"./hotelcomment/正面"): if filename.endswith(".txt"): with codecs.open("./hotelcomment/正面/"+filename, 'r', encoding='utf-8') as f: text = f.read() data.append(text) data_labels.append('pos') continue else: continue for filename in os.listdir(u"./hotelcomment/负面"): if filename.endswith(".txt"): with codecs.open(u"./hotelcomment/负面/"+filename, 'r', encoding='utf-8') as f: text = f.read() data.append(text) data_labels.append('neg') continue else: continue print(len(data), len(data_labels)) seg = pkuseg.pkuseg(model_name='web') cls_obj.vectorizer = CountVectorizer( analyzer = lambda text: seg.cut(text), lowercase = False, ) features = cls_obj.vectorizer.fit_transform( data ) features_nd = features.toarray() X_train, X_test, y_train, y_test = train_test_split( features_nd, data_labels, train_size=0.80, random_state=1234) cls_obj.log_model = LogisticRegression() cls_obj.log_model = cls_obj.log_model.fit(X=X_train, y=y_train) y_pred = cls_obj.log_model.predict(X_test) cls_obj.acc_score=accuracy_score(y_test, y_pred) return cls_obj

[ "bangtech@sina.com" ]

bangtech@sina.com

f91f09dca1cd6719bb83aa81dbb34abf79e48761

f0b75bd94f133a13f469f429a696f26be3be9862

/week_4/.history/class_exercise1_20200217114534.py

797ca5e3e1d3292edc31ea02837aa9efe73bbf2a

[]

no_license

dechavez4/Python_handin_assignments

023350fabd212cdf2a4ee9cd301306dc5fd6bea0

82fd8c991e560c18ecb2152ea5a8fc35dfc3c608

refs/heads/master

2023-01-11T23:31:27.220757

2020-05-22T10:33:56

237,179,899

0

null

2022-12-30T20:14:04

2020-01-30T09:30:16

Python

UTF-8

Python

false

1,235

py

import numpy as np a = np.arange(10,30).reshape(4,5) #exercise 1 table yellow = a[0,0] green = a[:3, 2] teal = a[:, (1,3)] blue = a[::2, 4] red = a[0, 1:4] #print('yellow= ', yellow, 'green= ', green, 'blue= ', blue, 'teal=', teal, 'red=', red) #exercise 2 cube: c = np.arange(0, 27).reshape((3, 3, 3)) # = (z, y, x) slice1 = c[1, 1, :] slice2 = c[:, 1 , 0 ] slice3 = c[0, :, 2] #print('slice1 = ', slice1, 'slice2 = ', slice2, 'slice3 = ', slice3) #exercise 3 masking: data = np.arange(1,101).reshape(10,10) even = data[data % 2 == 0] sixOnly = np.where(data % 10 == 6) six = data[sixOnly] #print('even =', even, 'sixOnly', six) #exercise 4 numpy and csv: filename = 'befkbhalderstatkode.csv' bef_stats_df = np.genfromtxt(filename, delimiter=',', dtype=np.uint, skip_header=1) dd = bef_stats_df mask_year_2015 = dd[:, 0] == 2015 mask_german = dd[:,3] == 5180 german_children_mask = (mask_year_2015 & mask_german & (dd[:, 2] <= 0)) german_children = np.sum(dd[(german_children_mask)][:, 4]) #print(german_children) def showNum(arr, bydel, alder, statkode): parts = (dd[:,0] == arr) & (dd[:,3] == bydel) & (dd[:,2] <= alder) & (dd[:,1] <=bydel) partsData = dd[parts] print(partsData) showNum(2015, 2, 0, 5180)

[ "chavezgamingv2@hotmail.com" ]

chavezgamingv2@hotmail.com

e976cba53c1c34192f3fe8310a36c701a6966fc2

fcdfe976c9ed60b18def889692a17dc18a8dd6d7

/python/geometry/polygon/line_line_intersect.py

bb815c566278010dd4858fcbc86ec08f82250541

[]

no_license

akihikoy/ay_test

4907470889c9bda11cdc84e8231ef3156fda8bd7

a24dfb720960bfedb94be3b4d147e37616e7f39a

refs/heads/master

2023-09-02T19:24:47.832392

2023-08-27T06:45:20

181,903,332

6

3

null

UTF-8

Python

false

25

py

../line_line_intersect.py

[ "info@akihikoy.net" ]

info@akihikoy.net

bf6d916adf0631e19932c2e5f3d01cddfc18a72e

ee409ec2e421bdac5988fcbe6592b05824b51d58

/google-datacatalog-qlik-connector/tests/google/datacatalog_connectors/qlik/scrape/engine_api_dimensions_helper_test.py

da581d76cc0bbe1954ca4808e0652eabc188a810

[ "Apache-2.0", "Python-2.0" ]

permissive

GoogleCloudPlatform/datacatalog-connectors-bi

7b11ed25856e83c8bd4b701dd836e0d20815caf7

58cc57e12632cbd1e237b3d6930e519333c51f4e

refs/heads/master

2023-04-01T14:27:24.548547

2022-02-12T09:55:56

259,464,922

34

18

Apache-2.0

2022-02-12T09:55:57

2020-04-27T21:51:45

Python

UTF-8

Python

false

6,322

py

#!/usr/bin/python # # Copyright 2021 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio import unittest from unittest import mock from google.datacatalog_connectors.qlik.scrape import \ engine_api_dimensions_helper from . import scrape_ops_mocks class EngineAPIDimensionsHelperTest(unittest.TestCase): __SCRAPE_PACKAGE = 'google.datacatalog_connectors.qlik.scrape' __BASE_CLASS = f'{__SCRAPE_PACKAGE}.base_engine_api_helper' \ f'.BaseEngineAPIHelper' __HELPER_CLASS = f'{__SCRAPE_PACKAGE}.engine_api_dimensions_helper' \ f'.EngineAPIDimensionsHelper' def setUp(self): self.__helper = engine_api_dimensions_helper.EngineAPIDimensionsHelper( server_address='https://test-server', auth_cookie=mock.MagicMock()) @mock.patch(f'{__HELPER_CLASS}._EngineAPIDimensionsHelper__get_dimensions', lambda *args: None) @mock.patch(f'{__BASE_CLASS}._run_until_complete') def test_get_dimensions_should_raise_unknown_exception( self, mock_run_until_complete): mock_run_until_complete.side_effect = Exception self.assertRaises(Exception, self.__helper.get_dimensions, 'app_id') @mock.patch(f'{__HELPER_CLASS}._EngineAPIDimensionsHelper__get_dimensions', lambda *args: None) @mock.patch(f'{__BASE_CLASS}._run_until_complete') def test_get_dimensions_should_return_empty_list_on_timeout( self, mock_run_until_complete): mock_run_until_complete.side_effect = asyncio.TimeoutError dimensions = self.__helper.get_dimensions('app-id') self.assertEqual(0, len(dimensions)) # BaseEngineAPIHelper._hold_websocket_communication is purposefully not # mocked in this test case in order to simulate a full send/reply scenario # with replies representing an App with Dimensions. Maybe it's worth # refactoring it in the future to mock that method, and the private async # ones from EngineAPIDimensionsHelper as well, thus testing in a more # granular way. @mock.patch(f'{__BASE_CLASS}._generate_message_id') @mock.patch(f'{__BASE_CLASS}._send_get_all_infos_message') @mock.patch(f'{__BASE_CLASS}._BaseEngineAPIHelper__send_open_doc_message') @mock.patch(f'{__BASE_CLASS}._connect_websocket', new_callable=scrape_ops_mocks.AsyncContextManager) def test_get_dimensions_should_return_list_on_success( self, mock_websocket, mock_send_open_doc, mock_send_get_all_infos, mock_generate_message_id): mock_send_open_doc.return_value = asyncio.sleep(delay=0, result=1) mock_send_get_all_infos.return_value = asyncio.sleep(delay=0, result=2) mock_generate_message_id.side_effect = [3, 4] websocket_ctx = mock_websocket.return_value.__enter__.return_value websocket_ctx.set_itr_break(0.25) websocket_ctx.set_data([ { 'id': 1, 'result': { 'qReturn': { 'qHandle': 1, }, }, }, { 'id': 2, 'result': { 'qInfos': [{ 'qId': 'dimension-id', 'qType': 'dimension' }], }, }, { 'id': 3, 'result': { 'qReturn': { 'qHandle': 2, }, }, }, { 'id': 4, 'result': { 'qProp': [{ 'qInfo': { 'qId': 'dimension-id', }, }], }, }, ]) dimensions = self.__helper.get_dimensions('app-id') self.assertEqual(1, len(dimensions)) self.assertEqual('dimension-id', dimensions[0].get('qInfo').get('qId')) mock_send_open_doc.assert_called_once() mock_send_get_all_infos.assert_called_once() # BaseEngineAPIHelper._hold_websocket_communication is purposefully not # mocked in this test case in order to simulate a full send/reply scenario # with replies representing an App with no Dimensions. Maybe it's worth # refactoring it in the future to mock that method, and the private async # ones from EngineAPIDimensionsHelper as well, thus testing in a more # granular way. @mock.patch(f'{__BASE_CLASS}._send_get_all_infos_message') @mock.patch(f'{__BASE_CLASS}._BaseEngineAPIHelper__send_open_doc_message') @mock.patch(f'{__BASE_CLASS}._connect_websocket', new_callable=scrape_ops_mocks.AsyncContextManager) def test_get_dimensions_should_return_empty_list_on_none_available( self, mock_websocket, mock_send_open_doc, mock_send_get_all_infos): mock_send_open_doc.return_value = asyncio.sleep(delay=0, result=1) mock_send_get_all_infos.return_value = asyncio.sleep(delay=0, result=2) websocket_ctx = mock_websocket.return_value.__enter__.return_value websocket_ctx.set_itr_break(0.25) websocket_ctx.set_data([ { 'id': 1, 'result': { 'qReturn': { 'qHandle': 1, }, }, }, { 'id': 2, 'result': { 'qInfos': [], }, }, ]) dimensions = self.__helper.get_dimensions('app-id') self.assertEqual(0, len(dimensions)) mock_send_open_doc.assert_called_once() mock_send_get_all_infos.assert_called_once()

[ "noreply@github.com" ]

GoogleCloudPlatform.noreply@github.com

6c7b94252cf23796c1c645176f35159465ceabce

33cf73bf603ffe09ad763fca4103e979ed50a4bc

/service_api/cd/NightWorkSpider.py

43e27efb13f5b1eaca5c928bbe078a11de05959a

[]

no_license

daddvted/excavat0r

f73d05670766d5f47ef5d7e443289851fc172906

8c2c56b6395bede4135fd859b1338831345054b6

refs/heads/master

2022-06-09T11:51:34.461893

2018-12-12T10:06:42

null

0

null

UTF-8

Python

false

7,876

py

""" 夜间施工查询 URL: http://www.cdcc.gov.cn/QualitySafeShow/NightWorkList.aspx """ import re import time import random import requests import lxml.html import mysql.connector from urllib.parse import urlencode class NightWorkSpider(object): USER_AGENTS = [ "Mozilla/5.0 (Windows NT 6.1; WOW64; rv:43.0) Gecko/20100101 Firefox/43.0", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.86 Safari/537.36", "Mozilla/5.0 (Linux; U; Android 4.4.4; zh-cn; MI NOTE LTE Build/KTU84P) AppleWebKit/533.1 (KHTML, like Gecko)Version/4.0 MQQBrowser/5.4 TBS/025489 Mobile Safari/533.1 MicroMessenger/6.3.13.49_r4080b63.740 NetType/cmnet Language/zh_CN", "Mozilla/5.0 (iPhone; CPU iPhone OS 9_2_1 like Mac OS X) AppleWebKit/601.1.46 (KHTML, like Gecko) Mobile/13D15 MicroMessenger/6.3.13 NetType/WIFI Language/zh_CN", "Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; WOW64; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; Shuame; .NET4.0C; .NET4.0E)", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Maxthon/4.9.1.1000 Chrome/39.0.2146.0 Safari/537.36", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.86 Safari/537.36", "Mozilla/5.0 (X11; U; Linux x86_64; en-US; rv:1.9.2.13) Gecko/20101209 Firefox/3.6.13", "Mozilla/4.0 (compatible; MSIE 9.0; Windows NT 5.1; Trident/5.0)", "Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 5.1; Trident/4.0; .NET CLR 1.1.4322; .NET CLR 2.0.50727)", "Mozilla/4.0 (compatible; MSIE 7.0b; Windows NT 6.0)", "Mozilla/5.0 (Windows; U; Windows NT 6.1; ru; rv:1.9.2.3) Gecko/20100401 Firefox/4.0 (.NET CLR 3.5.30729)", "Mozilla/5.0 (X11; U; Linux x86_64; en-US; rv:1.9.2.8) Gecko/20100804 Gentoo Firefox/3.6.8", "Mozilla/5.0 (X11; U; Linux x86_64; en-US; rv:1.9.2.7) Gecko/20100809 Fedora/3.6.7-1.fc14 Firefox/3.6.7", "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; .NET CLR 1.1.4322; .NET CLR 2.0.50727; .NET CLR 3.0.04506.30)", "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; .NET CLR 1.1.4322)", "Googlebot/2.1 (http://www.googlebot.com/bot.html)", "Opera/9.20 (Windows NT 6.0; U; en)", "Mozilla/5.0 (X11; U; Linux i686; en-US; rv:1.8.1.1) Gecko/20061205 Iceweasel/2.0.0.1 (Debian-2.0.0.1+dfsg-2)", "Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1667.0 Safari/537.36", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.103 Safari/537.36", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.11; rv:47.0) Gecko/20100101 Firefox/47.0", ] config = { 'user': 'root', 'password': 'hello', 'host': '192.168.86.86', 'port': '3306', 'database': 'service_cd', 'raise_on_warnings': True, } URL = "http://www.cdcc.gov.cn/QualitySafeShow/NightWorkList.aspx" BASE_URL = "http://www.cdcc.gov.cn/QualitySafeShow/" def __init__(self): self.total_page = 0 self.urls = [] self.__VIEWSTATE = "" self.__EVENTVALIDATION = "" self.__EVENTTARGET = "" self.cookie = "" self.crawl_date = time.strftime('%Y%m%d', time.localtime()) # Init mysql self.conn = mysql.connector.connect(**self.config) self.cursor = self.conn.cursor() def save2db(self, data): template = "INSERT INTO nightwork(unit, project, part, start, end, addr, crawl_date) " \ "VALUES (%(unit)s, %(project)s, %(part)s, %(start)s, %(end)s, %(addr)s, %(crawl_date)s)" self.cursor.execute(template, data) self.conn.commit() # 1st crawl, get total def crawl(self): print("crawling page 1") headers = { "User-Agent": random.choice(self.USER_AGENTS) } browser = requests.get(self.URL, headers=headers) if browser.status_code == 200: session = browser.cookies.get("ASP.NET_SessionId") self.cookie = "ASP.NET_SessionId=" + session html = lxml.html.fromstring(browser.text) # Crawl urls of 1st page links = html.xpath('//table[@id="DgList"]/tr/td[2]/a') for link in links: self.urls.append(self.BASE_URL + str(link.attrib["href"])) page_div = html.xpath('//div[@id="Navigate_divPanel"]/span') if len(page_div): tmp = str(page_div[0].text_content()) match = re.findall(r'(\d+)', tmp) self.total_page = int(match[0]) view_state_div = html.xpath('//input[@id="__VIEWSTATE"]') self.__VIEWSTATE = view_state_div[0].attrib["value"] event_valid_div = html.xpath('//input[@id="__EVENTVALIDATION"]') self.__EVENTVALIDATION = event_valid_div[0].attrib["value"] self.__EVENTTARGET = "Navigate$btnNavNext" self.crawl_step2() # Only 1 page, start final_crawl() else: self.final_crawl() else: print("Error while crawling page 1") self.crawl_step2() def crawl_step2(self): for p in range(2, self.total_page + 1): data = { "__VIEWSTATE": self.__VIEWSTATE, "__EVENTVALIDATION": self.__EVENTVALIDATION, "__EVENTTARGET": self.__EVENTTARGET, } print("crawling page {}".format(p)) headers = { "Content-Type": "application/x-www-form-urlencoded", "User-Agent": random.choice(self.USER_AGENTS), "Cookie": self.cookie } browser = requests.post(self.URL, headers=headers, data=urlencode(data)) if browser.status_code == 200: html = lxml.html.fromstring(browser.text) view_state_div = html.xpath('//input[@id="__VIEWSTATE"]') self.__VIEWSTATE = view_state_div[0].attrib["value"] event_valid_div = html.xpath('//input[@id="__EVENTVALIDATION"]') self.__EVENTVALIDATION = event_valid_div[0].attrib["value"] self.__EVENTTARGET = "Navigate$btnNavNext" links = html.xpath('//table[@id="DgList"]/tr/td[2]/a') for link in links: self.urls.append(self.BASE_URL + str(link.attrib["href"])) self.final_crawl() else: print("Error while crawling page {}".format(p)) self.final_crawl() def final_crawl(self): for url in self.urls: print("Crawling url: {}".format(url)) headers = { "User-Agent": random.choice(self.USER_AGENTS) } browser = requests.get(url, headers=headers) if browser.status_code == 200: html = lxml.html.fromstring(browser.text) tds = html.xpath('//table[@id="viewTable"]/tr/td[2]') data = { "unit": str(tds[0].text_content()), "project": str(tds[1].text_content()), "part": str(tds[2].text_content()), "start": str(tds[3].text_content()), "end": str(tds[4].text_content()), "addr": str(tds[5].text_content()), "crawl_date": self.crawl_date } self.save2db(data) else: print("Error while crawling url: {}".format(url)) if __name__ == "__main__": spider = NightWorkSpider() spider.crawl() spider.cursor.close() spider.conn.close()

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ski2per@163.com

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/users/apps.py

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[]

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cleliofavoccia/PurBeurre

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2023-03-20T11:06:32.466520

2021-03-12T16:02:22

331,650,830

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"""Manage users app""" from django.apps import AppConfig class UserConfig(AppConfig): name = 'users'

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2023-01-23T22:30:44.839942

2022-10-23T01:05:48

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from collections import defaultdict import numpy as np import tree # pip install dm_tree from typing import Dict from ray.rllib.utils.annotations import DeveloperAPI from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID from ray.rllib.utils.typing import PolicyID # Instant metrics (keys for metrics.info). LEARNER_INFO = "learner" # By convention, metrics from optimizing the loss can be reported in the # `grad_info` dict returned by learn_on_batch() / compute_grads() via this key. LEARNER_STATS_KEY = "learner_stats" @DeveloperAPI class LearnerInfoBuilder: def __init__(self, num_devices: int = 1): self.num_devices = num_devices self.results_all_towers = defaultdict(list) self.is_finalized = False def add_learn_on_batch_results( self, results: Dict, policy_id: PolicyID = DEFAULT_POLICY_ID, ) -> None: """Adds a policy.learn_on_(loaded)?_batch() result to this builder. Args: results: The results returned by Policy.learn_on_batch or Policy.learn_on_loaded_batch. policy_id: The policy's ID, whose learn_on_(loaded)_batch method returned `results`. """ assert ( not self.is_finalized ), "LearnerInfo already finalized! Cannot add more results." # No towers: Single CPU. if "tower_0" not in results: self.results_all_towers[policy_id].append(results) # Multi-GPU case: else: self.results_all_towers[policy_id].append( tree.map_structure_with_path( lambda p, *s: _all_tower_reduce(p, *s), *( results.pop("tower_{}".format(tower_num)) for tower_num in range(self.num_devices) ) ) ) for k, v in results.items(): if k == LEARNER_STATS_KEY: for k1, v1 in results[k].items(): self.results_all_towers[policy_id][-1][LEARNER_STATS_KEY][ k1 ] = v1 else: self.results_all_towers[policy_id][-1][k] = v def add_learn_on_batch_results_multi_agent( self, all_policies_results: Dict, ) -> None: """Adds multiple policy.learn_on_(loaded)?_batch() results to this builder. Args: all_policies_results: The results returned by all Policy.learn_on_batch or Policy.learn_on_loaded_batch wrapped as a dict mapping policy ID to results. """ for pid, result in all_policies_results.items(): if pid != "batch_count": self.add_learn_on_batch_results(result, policy_id=pid) def finalize(self): self.is_finalized = True info = {} for policy_id, results_all_towers in self.results_all_towers.items(): # Reduce mean across all minibatch SGD steps (axis=0 to keep # all shapes as-is). info[policy_id] = tree.map_structure_with_path( _all_tower_reduce, *results_all_towers ) return info def _all_tower_reduce(path, *tower_data): """Reduces stats across towers based on their stats-dict paths.""" # TD-errors: Need to stay per batch item in order to be able to update # each item's weight in a prioritized replay buffer. if len(path) == 1 and path[0] == "td_error": return np.concatenate(tower_data, axis=0) elif tower_data[0] is None: return None if isinstance(path[-1], str): # Min stats: Reduce min. if path[-1].startswith("min_"): return np.nanmin(tower_data) # Max stats: Reduce max. elif path[-1].startswith("max_"): return np.nanmax(tower_data) if np.isnan(tower_data).all(): return np.nan # Everything else: Reduce mean. return np.nanmean(tower_data)

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vakker.noreply@github.com

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/proof_of_work/deep_q/v0/deepqagentv0.py

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michaelneuder/parkes_lab_fa19

e68247ad5253d54f4d6074593a0e63fe61fcfc18

18d9f564e0df9c17ac5d54619ed869d778d4f6a4

refs/heads/master

2020-07-12T10:32:15.585380

2020-01-26T21:45:05

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import copy from environmentv0 import Environment from keras.models import clone_model from keras.models import Sequential from keras.layers import Dense from keras.optimizers import Adam import matplotlib.pyplot as plt plt.style.use('seaborn-muted') import numpy as np import progressbar import time import util np.random.seed(0) class DeepQLearningAgent(object): def __init__(self, discount, alpha, T, rho): # MDP self.alpha = alpha self.T = T self.rho = rho self.exploration_rate = 1 self.exploration_decrease = float(1e-5) self.min_exploration_rate = 0.1 # deep q self.learning_rate = 0.001 self.value_model = util.createModel(self.learning_rate) self.target_model = clone_model(self.value_model) self.target_model.set_weights(self.value_model.get_weights()) self.learning_update_count = 0 self.max_learning_steps = int(4e4) self.memories = [] self.training_memory_count = 32 self.discount = discount self.update_target_frequency = 1000 self.max_memory_count = 10000 self.min_memory_count_learn = 1000 # environment self.env = Environment(self.alpha, self.T) # visualization self.states_visited = np.zeros((self.T+1, self.T+1)) self.steps_before_done = [] self.last_50_steps = [] self.snyc_points = [] self.timing_between_updates = [] self.net_training_time = [] # timing self.last_target_net_clone = time.time() def chooseAction(self, current_state): # explore based on number of visits to that state. self.exploration_rate -= self.exploration_decrease current_explore_rate = self.exploration_rate if self.exploration_rate < self.min_exploration_rate: current_explore_rate = self.min_exploration_rate if np.random.uniform() < current_explore_rate: return np.random.randint(low=0, high=3) return np.argmax(self.value_model.predict(util.prepareInput(current_state))) def syncModels(self): self.target_model = clone_model(self.value_model) self.target_model.set_weights(self.value_model.get_weights()) def learn(self, iterations=10000): start_time = time.time() while self.learning_update_count < self.max_learning_steps: self.runTrial() print("total time {:.04f} s".format(time.time() - start_time)) def runTrial(self): done = False self.env.reset() step_counter = 0 while (not done) and (self.learning_update_count < self.max_learning_steps): step_counter += 1 current_state = self.env.current_state self.states_visited[current_state] += 1 # take action action = self.chooseAction(current_state) new_state, reward, done = self.env.takeAction(action) reward_value = util.evalReward(self.rho, reward) # creating a new memory memory = dict({ 'current_state' : current_state, 'action' : action, 'reward' : reward_value, 'new_state' : new_state, 'done' : done }) self.memories.append(memory) # training network if len(self.memories) > self.min_memory_count_learn: start_training = time.time() self.trainNeuralNet() self.net_training_time.append(time.time() - start_training) self.learning_update_count += 1 # keep memory list finite if len(self.memories) > self.max_memory_count: self.memories.pop(0) # update models if self.learning_update_count % self.update_target_frequency == 0: print('global step: {}. syncing models'.format(self.learning_update_count)) update_time = time.time() - self.last_target_net_clone self.timing_between_updates.append(update_time) print(' last synced: {:.04f} s ago'.format(update_time)) updates_remaining = (self.max_learning_steps - self.learning_update_count)/ self.update_target_frequency print(' eta: {:.02f} s'.format(updates_remaining * update_time)) print('*'*30) self.syncModels() self.value_model.save('saved_models/value_net_iter{0:06d}.h5'.format(self.learning_update_count)) self.snyc_points.append(self.learning_update_count) self.last_50_steps.append(np.mean(self.steps_before_done[-50:])) self.last_target_net_clone = time.time() self.steps_before_done.append(step_counter) def trainNeuralNet(self): memory_subset_indeces = np.random.randint(low=0, high=len(self.memories), size=self.training_memory_count) memory_subset = [self.memories[i] for i in memory_subset_indeces] rewards = [] current_states = [] new_states = [] actions = [] dones = [] for memory in memory_subset: rewards.append(memory['reward']) current_states.append(memory['current_state']) new_states.append(memory['new_state']) actions.append(memory['action']) dones.append(memory['done']) current_state_predictions = np.zeros((len(current_states), 3)) new_states_prepped = util.prepareInputs(new_states) # new_state_predictions = self.target_model.predict(new_states_prepped) new_state_predictions = [[1,1,1]] for i in range(len(new_state_predictions)): total_reward = rewards[i] if not dones[i]: total_reward += self.discount * max(new_state_predictions[i]) # clip if total_reward > 1: total_reward = 1 elif total_reward < -1: total_reward = -1 current_state_predictions[i][actions[i]] = total_reward # fiting model --- this is the neural net training self.value_model.fit( np.squeeze(np.asarray(current_states)), np.squeeze(np.asarray(current_state_predictions)), epochs=1, verbose=False) def main(): qlagent = DeepQLearningAgent(discount=0.99, alpha=0.45, T=9 , rho=0.6032638549804688) qlagent.learn(iterations=int(5000)) print(qlagent.exploration_rate) plt.plot(qlagent.net_training_time) plt.show() # results analyzer = util.ResultsAnalyzer( qlagent.value_model, qlagent.states_visited, qlagent.steps_before_done, qlagent.last_50_steps, qlagent.snyc_points, qlagent.timing_between_updates) end_policy = analyzer.extractPolicy() analyzer.processPolicy(end_policy) analyzer.plotStatesVisited(save=True) analyzer.plotLogStatesVisited(save=True) analyzer.plotStepsCounter(save=True) analyzer.plotExploration(save=True) analyzer.plotLast50(save=True) analyzer.plotTimings(save=True) if __name__ == "__main__": main()

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michael.neuder@gmail.com

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dr-dos-ok/Code_Jam_Webscraper

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26a35bf114a3aa30fc4c677ef069d95f41665cc0

refs/heads/master

2020-04-06T08:17:40.938460

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#!/usr/bin/env python import gmpy,math import sys f=sys.stdin n=int(f.next()) class Case(object): def __init__(self): self.res = "IMPOSSIBLE" N,M,A = map(int,f.next().split()) if N*M < A: return for xb in range(N+1): for yb in range(M+1): for xc in range(yb,N+1): for yc in range(xb,M+1): if abs(xb*yc - xc*yb) == A: self.res = "%s %s %s %s %s %s"%(0,0,xb,yb,xc,yc) return def run(self): pass def __str__(self): return str(self.res) for case in range(1, n+1): c=Case() c.run() print "Case #%s: %s"%(case,c)

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miliar1732@gmail.com

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/5690-ClosestDessertCost.py

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[]

no_license

Scott-Larsen/LeetCode

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refs/heads/main

2021-06-22T23:02:06.515527

2021-06-12T16:30:31

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# 5690. Closest Dessert Cost # You would like to make dessert and are preparing to buy the ingredients. You have n ice cream base flavors and m types of toppings to choose from. You must follow these rules when making your dessert: # There must be exactly one ice cream base. # You can add one or more types of topping or have no toppings at all. # There are at most two of each type of topping. # You are given three inputs: # baseCosts, an integer array of length n, where each baseCosts[i] represents the price of the ith ice cream base flavor. # toppingCosts, an integer array of length m, where each toppingCosts[i] is the price of one of the ith topping. # target, an integer representing your target price for dessert. # You want to make a dessert with a total cost as close to target as possible. # Return the closest possible cost of the dessert to target. If there are multiple, return the lower one. class Solution: def closestCost( self, baseCosts: List[int], toppingCosts: List[int], target: int ) -> int: combos = set(baseCosts) for topping in toppingCosts: cmbs = list(combos) for c in cmbs: combos.add(topping + c) combos.add(2 * topping + c) if target in combos: return target i = 1 while i <= target: if target - i in combos: return target - i elif target + i in combos: return target + i i += 1 return min(baseCosts)

[ "scott@scottlarsen.com" ]

scott@scottlarsen.com

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/13/utils/scanners.py

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Magnificent-Big-J/advent-of-code-2017

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refs/heads/master

2021-09-01T06:59:10.604222

2017-12-23T14:52:22

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def load_scanners(): layers = {} layer = 0 with open('input.txt') as f: for line in f: data = [int(i) for i in line.split(': ')] while layer != data[0]: layers[layer] = {'s': -1, 'd': -1, 'dir': None} layer += 1 layers[data[0]] = {'s': 0, 'd': data[1], 'dir': 'down'} layer += 1 return layers def move_scanners(layers): for j in layers: if layers[j]['dir'] == 'down': if layers[j]['s'] < (layers[j]['d'] - 1): layers[j]['s'] += 1 else: layers[j]['s'] -= 1 layers[j]['dir'] = 'up' elif layers[j]['dir'] == 'up': if layers[j]['s'] > 0: layers[j]['s'] -= 1 else: layers[j]['s'] += 1 layers[j]['dir'] = 'down' return layers

[ "chris@chrxs.net" ]

chris@chrxs.net

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/1047. Remove All Adjacent Duplicates In String/Solution_栈.py

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Inpurple/Leetcode

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df2bcca72fd303100dbcd73d1dfae44467abbb44

refs/heads/master

2020-05-20T02:17:08.430557

2019-09-22T07:51:28

185,327,908

3

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class Solution(object): def removeDuplicates(self, S): """ :type S: str :rtype: str """ sta=[] for i in S: if sta and sta[-1]==i: sta.pop() else: sta.append(i) return ''.join(sta)

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/164.生成一个随机的8位密码,要求4个字母和4个数字.py

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no_license

maohaoyang369/Python_exercise

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refs/heads/master

2020-04-09T23:04:02.327118

2019-09-05T14:49:07

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2019-03-21T14:44:13

2018-12-06T08:50:19

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py

# !/usr/bin/env python # -*- coding: utf-8 -*- # 生成一个随机的8位密码，要求4个字母和4个数字 import random import string spam_num = random.choices("0123456789", k=4) print(spam_num) spam_letters = random.sample(string.ascii_letters, 4) print(spam_letters) spam = spam_num+spam_letters print(spam) spam_num_letters = random.shuffle(spam) print(spam) secrity = "".join(spam) print(secrity)

[ "372713573@qq.com" ]

372713573@qq.com

07ed60f2ac262214e2aa84b74db7f7fd479050c3

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/pylmp/InKim/BGF_mergeBgf.py

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[]

no_license

hopefulp/sandbox

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4d26767f287be6abc88dc74374003b04d509bebf

refs/heads/master

2023-06-27T17:50:16.637851

2023-06-15T03:53:39

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2022-09-13T13:22:34

2019-10-29T05:14:02

C++

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#!/opt/applic/epd/bin/python import sys, re, string, getopt, optparse, math, time from os import popen option = ""; args = ""; bgf_file = ""; mod_file = ""; out_file = "" usage = """ Usage: mergeBGF.py -b bgf1_file -c bgf2_file -o out_file """ options, args = getopt.getopt(sys.argv[1:], 'hb:c:o:', ['help','bgf1=','bgf2=','out=']) for option, value in options: if option in ('-h', '--help'): print usage; sys.exit(0) elif option in ('-b', '--bgf1'): bgf1_file = value elif option in ('-c', '--bgf2'): bgf2_file = value elif option in ('-o', '--out'): out_file = value elif option in (''): print usage; sys.exit(0) #----------------- # merge two bgf file # #_________________ def mergebgf(bgf1_file, bgf2_file, out_file): print(options) # read bgf 1 and bgf 2 f_bgf1_file = open(bgf1_file) f_bgf2_file = open(bgf2_file) f_out_file = open(out_file,'w') bgf1_atom_data = []; bgf2_atom_data = []; bgf1_conect_data = []; bgf2_conect_data = [] n_atoms_1 = 0; n_atoms_2 = 0 while 1: line = f_bgf1_file.readline() if not line: break if 'HETATM' in line: n_atoms_1 += 1 parse = re.split('\s*', line) bgf1_atom_data.append(parse) if 'FORMAT' in line: continue if 'CONECT' in line: parse = re.split('\s*', line) parse = parse[:-1] bgf1_conect_data.append(parse) while 1: line = f_bgf2_file.readline() if not line: break if 'HETATM' in line: n_atoms_2 += 1 parse = re.split('\s*', line) bgf2_atom_data.append(parse) if 'FORMAT' in line: continue if 'CONECT' in line: parse = re.split('\s*', line) parse = parse[:-1] bgf2_conect_data.append(parse) # add n_atom_1 to atom id of bgf 2 #margin = int(math.ceil(n_atoms_1 / 10.0)*10) #print(margin) margin = n_atoms_1 for atom in bgf2_atom_data: atom[1] = str(int(atom[1]) + margin) for conect in bgf2_conect_data: n_conect = len(conect) for i in xrange(1, n_conect): conect[i] = str(int(conect[i]) + margin) # merge the file sequentially: 1 -> 2 f_bgf1_file.seek(0) f_bgf2_file.seek(0) # header while 1: line = f_bgf1_file.readline() if not line: break if 'HETATM' in line: break f_out_file.write(line) # atom data of bgf1 for item in bgf1_atom_data: item[6] = float(item[6]) item[7] = float(item[7]) item[8] = float(item[8]) item[12] = float(item[12]) wline = '{0:>6} {1:>5} {2:<5} {3:3} {4:<1}{5:>5} {6:>10.5f}{7:>10.5f}{8:>10.5f} {9:<5}{10:3}{11:2} {12:>8.5f}'.format(*item) wline += '\n' f_out_file.write(wline) # atom data of bgf2 for item in bgf2_atom_data: item[6] = float(item[6]) item[7] = float(item[7]) item[8] = float(item[8]) item[12] = float(item[12]) wline = '{0:>6} {1:>5} {2:<5} {3:3} {4:<1}{5:>5} {6:>10.5f}{7:>10.5f}{8:>10.5f} {9:<5}{10:3}{11:2} {12:>8.5f}'.format(*item) wline += '\n' f_out_file.write(wline) f_out_file.write('FORMAT CONECT (a6,12i6)\n') wline = "" for item in bgf1_conect_data: for i in xrange(0, len(item)): wline += '{0:>6}'.format(item[i]) wline += '\n' f_out_file.write(wline) wline = "" for item in bgf2_conect_data: for i in xrange(0, len(item)): wline += '{0:>6}'.format(item[i]) wline += '\n' f_out_file.write(wline) f_out_file.write("END\n") f_out_file.write("") f_out_file.close() #return 1 # main call mergebgf(bgf1_file, bgf2_file, out_file)

[ "hopefulp@gmail.com" ]

hopefulp@gmail.com

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/mongo/src/conn.py

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HackUPCCrew/MachineLearning

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refs/heads/master

2021-07-14T01:25:59.521438

2017-10-17T19:35:45

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#!/usr/bin/env python3 from pymongo import MongoClient from pprint import pprint client = MongoClient("mongodb://34.224.70.221:8080") db=client.admin serverStatusResult=db.command("serverStatus") pprint(serverStatusResult)

[ "krishnakalyan3@gmail.com" ]

krishnakalyan3@gmail.com

5c9f74d4f9302e90ca39b1dd80dce303ed88f773

aedd3aeadfb13eda4489d26ee3d9762598878936

/leetcode/1281. 整数的各位积和之差.py

f67fa9eb3bba6026574b965d007dd6e0b0c201b1

[]

no_license

AnJian2020/Leetcode

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cded97a52c422f98b55f2b3527a054d23541d5a4

refs/heads/master

2023-03-26T16:25:36.136647

2021-03-26T07:04:10

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class Solution: def subtractProductAndSum(self, n: int) -> int: numList=list(str(n)) sum=0 product=1 for item in range(len(numList)): numList[item]=int(numList[item]) sum+=numList[item] product*=numList[item] result=product-sum return result if __name__ == "__main__": print(Solution().subtractProductAndSum(4421))

[ "xuhao2018@foxmail.com" ]

xuhao2018@foxmail.com

9b4871a27d15086682164ca0e12198fdb16cab67

a4344e89e7f467d8bfd3f000f8cced17e36bfd70

/predict.py

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[]

no_license

Schnei1811/InsectClassifier

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refs/heads/master

2023-04-18T08:51:07.753666

2021-03-14T03:07:49

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import argparse import cv2 from glob import glob from tqdm import tqdm import numpy as np import os import torch, torchvision import torch.nn as nn from torchvision import models, transforms import json import csv # Number of classes in the dataset img_size = 224 class GlobalAvgPool2d(nn.Module): def forward (self, x): return torch.mean(x.view(x.size(0), x.size(1), -1), dim=2) def initialize_model(arch, num_classes): # Initialize these variables which will be set in this if statement. Each of these # variables is model specific. model_ft = None if arch == "resnet": """ Resnet101 """ model_ft = models.resnet101() num_ftrs = model_ft.fc.in_features model_ft.fc = nn.Linear(num_ftrs, num_classes) elif arch == "mobilenet": """ Mobilenet """ model_ft = models.mobilenet_v2() num_ftrs = model_ft.classifier[1].in_features elif arch == "densenet": """ Densenet """ model_ft = models.densenet201() #DenseNet201 num_ftrs = model_ft.classifier.in_features else: print(f"Unknown model name {arch}. Choose from resnet, mobilenet, or densenet") quit() model_ft.classifier = nn.Sequential( GlobalAvgPool2d(), #Equivalent to GlobalAvgPooling in Keras # nn.Linear(1920, 1024), nn.Linear(num_ftrs, 1024), nn.ReLU(), nn.Linear(1024, 1024), nn.ReLU(), nn.Linear(1024, 512), nn.ReLU(), nn.Linear(512, num_classes)) return model_ft class CustomDataset(torch.utils.data.Dataset): def __init__(self, X_images, X_paths): self.X_images = X_images self.X_paths = X_paths def __len__(self): return len(self.X_images) def __getitem__(self, idx): sample = self.X_images[idx] sample = sample.astype("float32") / 255.0 sample = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])(sample) return (sample, self.X_paths[idx]) def buildImageAspectRatio(X_path): img = cv2.imread(X_path) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) resize_x = int(img.shape[1] * img_size / max(img.shape)) resize_y = int(img.shape[0] * img_size / max(img.shape)) push_x = (img_size - resize_x) // 2 push_y = (img_size - resize_y) // 2 resized_img = cv2.resize(img, (resize_x, resize_y)) canvas = np.zeros((img_size, img_size, 3)).astype("uint8") + 255 canvas[push_y:resized_img.shape[0] + push_y, push_x:resized_img.shape[1] + push_x, :] = resized_img return canvas def createData(data_name, X_paths): if not os.path.exists("Arrays_Batches"): os.makedirs("Arrays_Batches") if not os.path.exists("Arrays_Data"): os.makedirs("Arrays_Data") reset = True data_batch = 0 for i, X_path in enumerate(tqdm(X_paths)): if reset == True: reset = False X = np.expand_dims(buildImageAspectRatio(X_path), axis=0) else: X = np.vstack((X, np.expand_dims(buildImageAspectRatio(X_path), axis=0))) if not i == 0 and i % 999 == 0: reset = True np.save(f"Arrays_Batches/{data_name}_Input_{data_batch}_{len(X)}.npy", X) data_batch += 1 if i == len(X_paths) - 1: np.save(f"Arrays_Batches/{data_name}_Input_{data_batch}_{len(X)}.npy", X) data_batch += 1 data_paths = [] for batch in range(data_batch): data_paths.append(glob(f'Arrays_Batches/{data_name}_Input_{batch}_*')[0]) for i, data_path in enumerate(tqdm(data_paths)): data = np.load(data_path) if i == 0: X = data else: X = np.vstack((X, data)) np.save(f'Arrays_Data/{data_name}_Input_{len(X)}.npy', X) def test_model(model, dataloader, device, num_to_class, report_csv): model.eval() preds_array = np.array([]) for inputs, paths in tqdm(dataloader): inputs = inputs.to(device) outputs = model(inputs) _, preds = torch.max(outputs, 1) preds_cpu = preds.cpu().numpy() preds_array = np.append(preds_array, preds_cpu) for i, pred in enumerate(preds_cpu): img_name = paths[i].split("/")[-1] report_csv.append([img_name, num_to_class[pred]]) csv_path = f"pred.csv" with open(csv_path, "w", newline="") as f: writer = csv.writer(f) writer.writerows(report_csv) def main(data_name, arch, model_name, batch_size): report_csv = [["file_path", "prediction (Order_Family)"]] with open(f"metadata/{data_name}_num_to_class.json") as f: num_to_class = json.load(f) num_to_class = {int(k):v for k,v in num_to_class.items()} num_classes = len(num_to_class) X_paths = glob("extracted/*") input_file_path = f"Arrays_Data/{data_name}_Input_{len(X_paths)}.npy" if not os.path.exists(input_file_path): createData(data_name, X_paths) X = np.load(input_file_path) image_dataset = CustomDataset(X, X_paths) dataloader = torch.utils.data.DataLoader(image_dataset, batch_size=batch_size, shuffle=True, num_workers=4) model_ft = initialize_model(arch, num_classes) # Detect if we have a GPU available # if torch.cuda.device_count() > 1: # print("Let's use", torch.cuda.device_count(), "GPUs!") model_ft = nn.DataParallel(model_ft) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model_ft = model_ft.to(device) model_path = os.path.join("models", arch, model_name) model_ft.load_state_dict(torch.load(model_path)) test_model(model_ft, dataloader, device, num_to_class, report_csv) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--data_name", default="Alus") parser.add_argument("--arch", default="mobilenet") #densenet, resnet, mobilenet parser.add_argument("--model_name", default="0_0.9765853658536585_450.pt") parser.add_argument("--batch_size", default=32, type=int) args = parser.parse_args() main(args.data_name, args.arch, args.model_name, args.batch_size)

[ "stefan871@gmail.com" ]

stefan871@gmail.com

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/yubinbai/pcuva-problems/UVa 10082 - WERTYU/main.py

f4cf71c10e1e6bc76858ecb5779421a0e7b80c6f

[]

no_license

xenron/sandbox-github-clone

364721769ea0784fb82827b07196eaa32190126b

5eccdd8631f8bad78eb88bb89144972dbabc109c

refs/heads/master

2022-05-01T21:18:43.101664

2016-09-12T12:38:32

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''' Created on Jun 18, 2013 @author: Yubin Bai All rights reserved. ''' import time from multiprocessing.pool import Pool parallelSolve = False INF = 1 << 30 def solve(par): r1 = '`1234567890-' + 'qwertyuiop[' + 'asdfghjhkl' + 'zxcvbnm,.' r2 = '1234567890-=' + 'wertyuiop[]' + 'sdfghjhkl;' + 'xcvbnm,./' d = {' ': ' '} for k, v in zip(r2, r1): d[k.upper()] = v.upper() word = par result = [] for c in word: result.append(d[c]) return ''.join(result) class Solver: def getInput(self): self.numOfTests = 1 self.input = [] word = self.fIn.readline().strip() self.input.append((word)) def __init__(self): self.fIn = open('input.txt') self.fOut = open('output.txt', 'w') self.results = [] def parallel(self): self.getInput() p = Pool(4) millis1 = int(round(time.time() * 1000)) self.results = p.map(solve, self.input) millis2 = int(round(time.time() * 1000)) print("Time in milliseconds: %d " % (millis2 - millis1)) self.makeOutput() def sequential(self): self.getInput() millis1 = int(round(time.time() * 1000)) for i in self.input: self.results.append(solve(i)) millis2 = int(round(time.time() * 1000)) print("Time in milliseconds: %d " % (millis2 - millis1)) self.makeOutput() def makeOutput(self): for test in range(self.numOfTests): self.fOut.write("Case #%d: %s\n" % (test + 1, self.results[test])) self.fIn.close() self.fOut.close() if __name__ == '__main__': solver = Solver() if parallelSolve: solver.parallel() else: solver.sequential()

[ "xenron@outlook.com" ]

xenron@outlook.com

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/question/migrations/0005_auto_20210510_1025.py

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no_license

lesage20/vuejs

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refs/heads/main

2023-04-19T12:20:54.672778

2021-05-12T08:42:34

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# Generated by Django 3.1.7 on 2021-05-10 10:25 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('question', '0004_question_titre'), ] operations = [ migrations.AlterField( model_name='question', name='prop', field=models.ManyToManyField(blank=True, null=True, related_name='question', to='question.Proposition'), ), ]

[ "angezanou00@gmail.com" ]

angezanou00@gmail.com

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/thonnycontrib/m5stack/esp8266_api_stubs/uhashlib.py

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permissive

thonny/thonny-m5stack

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refs/heads/master

2020-04-20T14:57:15.605699

2019-11-18T22:28:36

168,914,658

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class sha1: '' def digest(): pass def update(): pass class sha256: '' def digest(): pass def update(): pass

[ "aivar.annamaa@gmail.com" ]

aivar.annamaa@gmail.com

199dc7283ca9dfcf42146c61abe183d9955ad52c

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/week13/day1/daily/phonedir/views.py

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[]

no_license

jfrance00/di-exercises

623bebeddd3ff3ed062e1ad5097f15f7ed002362

bbc97714c26b41ed76dfed35df5780e3aa482b5e

refs/heads/master

2022-11-27T23:31:59.742231

2020-07-29T12:20:01

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import flask import wtforms as wtf from . import forms, models from . import app, db @app.route('/', methods=['GET', 'POST']) def index(): form = forms.SearchNameOrNumber() return flask.render_template('index.html', form=form) @app.route('/add-input', methods=['GET', 'POST']) def add_input(): form = forms.AddPersonToDatabase() if flask.request.method == "POST": id = form.id.data name = form.name.data phone = form.phone.data email = form.email.data address = form.address.data entry = models.Person(id=id, name=name, phone=phone, email=email, address=address) db.session.add(entry) db.session.commit() flask.flash(f'{name} added successfully') return flask.render_template('add-input.html', form=form)

[ "jfrance00@gmail.com" ]

jfrance00@gmail.com

f8eb219a525fd3d56b4f5fae1875cccf536032f1

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/dsn_purchase_order/models/purchase.py

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[]

no_license

disna-sistemas/odoo

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refs/heads/8.0

2022-03-08T19:01:21.162717

2022-02-15T13:06:26

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# -*- encoding: utf-8 -*- ############################################################################## # # OpenERP, Open Source Management Solution # Copyright (C) 2004-2012 OpenERP SA (<http://openerp.com>) # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # ############################################################################## from openerp import models, fields, api from openerp import tools, _ class dsnPurchaseOrder(models.Model): _inherit = "purchase.order" _order = "date_order desc, name" class dsnPurchaseOrderLine(models.Model): _inherit = "purchase.order.line" # _order = "date_planned desc, name" _order = "id" @api.multi @api.onchange('product_id') def dsn_warning_obsolete(self): self.ensure_one() res = {} if self.product_id: _obsolete = False if self.product.state and self.product_id.state=='obsolete': res = {'warning': {'title': _('Obsolete Product'), 'message': _( 'This product is obsolete')}} return res class dsnPurchasereport(models.Model): _inherit = "purchase.report" dsncat2_id = fields.Many2one(comodel_name='product.category', string='Cat2', readonly=True) def init(self, cr): tools.sql.drop_view_if_exists(cr, 'purchase_report') cr.execute(""" create or replace view purchase_report as ( WITH currency_rate (currency_id, rate, date_start, date_end) AS ( SELECT r.currency_id, r.rate, r.name AS date_start, (SELECT name FROM res_currency_rate r2 WHERE r2.name > r.name AND r2.currency_id = r.currency_id ORDER BY r2.name ASC LIMIT 1) AS date_end FROM res_currency_rate r ) select min(l.id) as id, s.date_order as date, l.state, s.date_approve, s.minimum_planned_date as expected_date, s.dest_address_id, s.pricelist_id, s.validator, spt.warehouse_id as picking_type_id, s.partner_id as partner_id, s.create_uid as user_id, s.company_id as company_id, l.product_id, t.categ_id as category_id, t.dsncat2_id, t.uom_id as product_uom, s.location_id as location_id, sum(l.product_qty/u.factor*u2.factor) as quantity, extract(epoch from age(s.date_approve,s.date_order))/(24*60*60)::decimal(16,2) as delay, extract(epoch from age(l.date_planned,s.date_order))/(24*60*60)::decimal(16,2) as delay_pass, count(*) as nbr, sum(l.price_unit/cr.rate*l.product_qty)::decimal(16,2) as price_total, avg(100.0 * (l.price_unit/cr.rate*l.product_qty) / NULLIF(ip.value_float*l.product_qty/u.factor*u2.factor, 0.0))::decimal(16,2) as negociation, sum(ip.value_float*l.product_qty/u.factor*u2.factor)::decimal(16,2) as price_standard, (sum(l.product_qty*l.price_unit/cr.rate)/NULLIF(sum(l.product_qty/u.factor*u2.factor),0.0))::decimal(16,2) as price_average from purchase_order_line l join purchase_order s on (l.order_id=s.id) left join product_product p on (l.product_id=p.id) left join product_template t on (p.product_tmpl_id=t.id) LEFT JOIN ir_property ip ON (ip.name='standard_price' AND ip.res_id=CONCAT('product.template,',t.id) AND ip.company_id=s.company_id) left join product_uom u on (u.id=l.product_uom) left join product_uom u2 on (u2.id=t.uom_id) left join stock_picking_type spt on (spt.id=s.picking_type_id) join currency_rate cr on (cr.currency_id = s.currency_id and cr.date_start <= coalesce(s.date_order, now()) and (cr.date_end is null or cr.date_end > coalesce(s.date_order, now()))) group by s.company_id, s.create_uid, s.partner_id, u.factor, s.location_id, l.price_unit, s.date_approve, l.date_planned, l.product_uom, s.minimum_planned_date, s.pricelist_id, s.validator, s.dest_address_id, l.product_id, t.categ_id, t.dsncat2_id, s.date_order, l.state, spt.warehouse_id, u.uom_type, u.category_id, t.uom_id, u.id, u2.factor ) """)

[ "sistemas@disna.com" ]

sistemas@disna.com

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/api/barriers/migrations/0038_auto_20200224_1622.py

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uktrade/market-access-api

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refs/heads/master

2023-08-30T14:47:10.373148

2023-08-29T13:58:08

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MIT

2023-09-14T08:04:42

2018-05-02T13:38:37

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# Generated by Django 2.2.8 on 2020-02-24 16:22 from django.db import migrations def populate_archived_reason(apps, schema_editor): BarrierInstance = apps.get_model("barriers", "BarrierInstance") BarrierInstance.objects.filter( archived=True, archived_reason__isnull=True, ).update(archived_reason="OTHER", archived_explanation="Archive reason unknown") def unpopulate_archived_reason(apps, schema_editor): BarrierInstance = apps.get_model("barriers", "BarrierInstance") BarrierInstance.objects.filter( archived=True, archived_reason="OTHER", archived_explanation="Archive reason unknown", ).update( archived_reason=None, archived_explanation=None, ) class Migration(migrations.Migration): dependencies = [ ("barriers", "0037_auto_20200224_1552"), ] operations = [ migrations.RunPython(populate_archived_reason, unpopulate_archived_reason), ]

[ "noreply@github.com" ]

uktrade.noreply@github.com

d38479e4f3d5d36e535a5c308876ea91eff7adfb

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/makewiki/settings.py

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LukazDane/makewiki_v2

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d71790c99951ed47d202e5a00d1eb7480b8552bd

refs/heads/master

2022-04-30T00:08:06.716424

2020-01-08T01:11:26

221,078,482

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MIT

2022-04-22T22:44:16

2019-11-11T22:04:27

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""" Django settings for makewiki project. Generated by 'django-admin startproject' using Django 2.2.7. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '1yct-t!2bnkgc7j59z+9cdd2k)@y+ftqor$!aya()3if^cnlo-' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ['localhost', 'makewiki-lh.herokuapp.com'] # Application definition INSTALLED_APPS = [ 'rest_framework', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'accounts.apps.AccountsConfig', # new 'wiki', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'django_currentuser.middleware.ThreadLocalUserMiddleware', ] ROOT_URLCONF = 'makewiki.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [ # Put strings here, like "/home/html/django_templates" or "C:/www/django/templates". # Always use forward slashes, even on Windows. # Don't forget to use absolute paths, not relative paths. os.path.join(BASE_DIR, 'templates').replace('\\', '/'), ], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'makewiki.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'wiki.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'America/Los_Angeles' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles') # wiki app settings WIKI_PAGE_TITLE_MAX_LENGTH = 600 # Where to redirect during authentication LOGIN_REDIRECT_URL = "/" LOGOUT_REDIRECT_URL = "/" DEFAULT_LOGOUT_URL = '/' # Required for Heroku SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https') # PROTIP: # Need to override settings? Create a local_settings.py file # in this directory, and add settings there. try: from makewiki.settings import * except ImportError: pass

[ "deandrevidal@aol.com" ]

deandrevidal@aol.com

51eb6471c303f10aa5f7d41241c0f542184c8c79

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/724. Find Pivot Index.py

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2023-08-25T12:31:08.436640

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class Solution: def pivotIndex(self, nums: List[int]) -> int: right_sum = sum(nums) left_sum = 0 prev = 0 for i in range(len(nums)) : left_sum += prev prev = nums[i] right_sum -= nums[i] if left_sum == right_sum : return i return -1

[ "todsapon.singsunjit@gmail.com" ]

todsapon.singsunjit@gmail.com

e6361dfa82714822273013df5ab2d96aacb6a6a4

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/python_udemy/introducao-python/iterando-strings-while.py

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[]

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marcelomatz/py-studiesRepo

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refs/heads/main

2023-09-05T00:03:47.712289

2021-06-15T09:43:27

null

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# iterar é passar por cada um dos elementos de uma string # se tem índice é iterável frase = 'o rato roeu a roupa do rei de roma' tamanho_frase = len(frase) contador = 0 nova_string = '' while contador < tamanho_frase: letra = frase[contador] if letra == 'r': nova_string += 'R' else: nova_string += letra contador += 1 print(nova_string)

[ "agenciahipster@gmail.com" ]

agenciahipster@gmail.com

dc910c5e544db2555849a7d275f3d49ddc8c3178

98c6ea9c884152e8340605a706efefbea6170be5

/examples/data/Assignment_9/nckkem001/question1.py

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[]

no_license

MrHamdulay/csc3-capstone

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refs/heads/master

2021-03-12T21:55:57.781339

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"""Program to analyse student marks from source file and determine which students are advised to consult an advisor. Kemeshan Naicker 11 May 2014""" #Prompt user for name of source file. file = input("Enter the marks filename:\n") #Open file for processing txtfile = open(file, "r") #Read file into a string, and replace newline characters with spaces in order to #read string into a list. markslist = txtfile.read() txtfile.close() markslist = markslist.split("\n") markslist = " ".join(markslist) markslist = markslist.split(",") markslist = " ".join(markslist) #Read string into a list. markslist = markslist.split() marks = [] students = [] for i in range (0, len(markslist), 2): students.append(markslist[i]) marks.append(eval(markslist[i+1])) #Calculate standard deviation. total = 0 N = len(marks) for i in marks: total += i avrg = total/N sdsum = 0 for i in marks: sdsum += (i - avrg)**2 sd = (sdsum/N)**(1/2) #Find students who are below one standard deviation of the mean and append them #to a new list. fail_list = [] for i in range(N): if marks[i] < (avrg - sd): fail_list.append(students[i]) #Print output. print("The average is: {0:0.2f}".format(avrg)) print("The std deviation is: {0:0.2f}".format(sd)) if len(fail_list) > 0: print("List of students who need to see an advisor:") for i in fail_list: print(i)

[ "jarr2000@gmail.com" ]

jarr2000@gmail.com

ddb61c4bdd3fe3a64465df7dd8e17bc6b3404d57

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/style_trans/neural_style.py

8261b661b74a941d3560532e80ad4643e2310592

[]

no_license

invoker4zoo/tensorflow_project

cf1f0fc2d7772a4da4004c1f0c14c5be8fcb909e

847e511f8fa4634181b00055a01c75b9b46c5396

refs/heads/master

2020-03-11T18:06:43.514220

2018-04-19T08:53:42

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# coding=utf-8 """ @ license: Apache Licence @ github: invoker4zoo @ author: invoker/cc @ wechat: whatshowlove @ software: PyCharm @ file: neural_style @ time: 18-4-10 """ import tensorflow as tf import reader import _reader from preprocessing import preprocessing_factory from nets import nets_factory import losses import time import os import utils # tf.app.flags.DEFINE_float("CONTENT_WEIGHT", 5e0, "Weight for content features loss") # tf.app.flags.DEFINE_float("STYLE_WEIGHT", 1e2, "Weight for style features loss") # tf.app.flags.DEFINE_float("TV_WEIGHT", 1e-5, "Weight for total variation loss") # tf.app.flags.DEFINE_string("VGG_MODEL", "pretrained/vgg_16.ckpt", "vgg model params path") # tf.app.flags.DEFINE_list("CONTENT_LAYERS", ["vgg_16/conv3/conv3_3"], # "Which VGG layer to extract content loss from") # tf.app.flags.DEFINE_list("STYLE_LAYERS", ["vgg_16/conv1/conv1_2", "vgg_16/conv2/conv2_2", # "vgg_16/conv3/conv3_3", "vgg_16/conv4/conv4_3"], # "Which layers to extract style from") # tf.app.flags.DEFINE_string("SUMMARY_PATH", "tensorboard", "Path to store Tensorboard summaries") # tf.app.flags.DEFINE_string("STYLE_IMAGE", "img/picasso.jpg", "Styles to train") # tf.app.flags.DEFINE_float("STYLE_SCALE", 1.0, "Scale styles. Higher extracts smaller features") # tf.app.flags.DEFINE_float("LEARNING_RATE", 10., "Learning rate") # tf.app.flags.DEFINE_string("CONTENT_IMAGE", "img/dancing.jpg", "Content image to use") # tf.app.flags.DEFINE_boolean("RANDOM_INIT", True, "Start from random noise") # tf.app.flags.DEFINE_integer("NUM_ITERATIONS", 1000, "Number of iterations") # # reduce image size because of cpu training # tf.app.flags.DEFINE_integer("IMAGE_SIZE", 256, "Size of output image") ####################################################################### tf.app.flags.DEFINE_string("loss_model", 'vgg_16', "loss model name") tf.app.flags.DEFINE_string("naming", 'test', "model_name") tf.app.flags.DEFINE_string("loss_model_file", "pretrained/vgg_16.ckpt", "pretrained model") tf.app.flags.DEFINE_string("checkpoint_exclude_scopes", "vgg_16/fc", "ignore variables") tf.app.flags.DEFINE_float("content_weight", 5, "Weight for content features loss") tf.app.flags.DEFINE_float("style_weight", 100, "Weight for style features loss") tf.app.flags.DEFINE_float("tv_weight", 0.0, "Weight for total variation loss") tf.app.flags.DEFINE_integer("image_size", 256, "Size of output image") tf.app.flags.DEFINE_list("content_layers", ["vgg_16/conv3/conv3_3"], "Which VGG layer to extract content loss from") tf.app.flags.DEFINE_list("style_layers", ["vgg_16/conv1/conv1_2", "vgg_16/conv2/conv2_2", "vgg_16/conv3/conv3_3", "vgg_16/conv4/conv4_3"], "Which layers to extract style from") tf.app.flags.DEFINE_string("model_path", 'models', "path to save model") tf.app.flags.DEFINE_string("content_image", "img/dancing.jpg", "Content image to use") tf.app.flags.DEFINE_string("style_image", "img/picasso.jpg", "Styles to train") tf.app.flags.DEFINE_float("learning_rate", 10, "Learning rate") tf.app.flags.DEFINE_integer("step", 100, "Number of iterations") FLAGS = tf.app.flags.FLAGS def total_variation_loss(layer): shape = tf.shape(layer) height = shape[1] width = shape[2] y = tf.slice(layer, [0,0,0,0], tf.stack([-1,height-1,-1,-1])) - tf.slice(layer, [0,1,0,0], [-1,-1,-1,-1]) x = tf.slice(layer, [0,0,0,0], tf.stack([-1,-1,width-1,-1])) - tf.slice(layer, [0,0,1,0], [-1,-1,-1,-1]) return tf.nn.l2_loss(x) / tf.to_float(tf.size(x)) + tf.nn.l2_loss(y) / tf.to_float(tf.size(y)) # TODO: Okay to flatten all style images into one gram? def gram(layer): shape = tf.shape(layer) num_filters = shape[3] size = tf.size(layer) filters = tf.reshape(layer, tf.stack([-1, num_filters])) gram = tf.matmul(filters, filters, transpose_a=True) / tf.to_float(size) return gram # TODO: Different style scales per image. def get_style_features(style_paths, style_layers): with tf.Graph().as_default() as g: network_fn = nets_factory.get_network_fn( FLAGS.loss_model, num_classes=1, is_training=False) image_preprocessing_fn, image_unprocessing_fn = preprocessing_factory.get_preprocessing( FLAGS.loss_model, is_training=False) image = tf.expand_dims( reader.get_image(FLAGS.style_image, FLAGS.image_size, FLAGS.image_size, image_preprocessing_fn), 0) # image = tf.expand_dims( # _reader.get_image(FLAGS.content_image, FLAGS.image_size), 0) _, endpoints = network_fn(image, spatial_squeeze=False) features = [] for layer in style_layers: features.append(gram(endpoints[layer])) with tf.Session() as sess: init_func = utils._get_init_fn(FLAGS) init_func(sess) return sess.run(features) def get_content_features(content_path, content_layers): with tf.Graph().as_default() as g: network_fn = nets_factory.get_network_fn( FLAGS.loss_model, num_classes=1, is_training=False) image_preprocessing_fn, image_unprocessing_fn = preprocessing_factory.get_preprocessing( FLAGS.loss_model, is_training=False) image = tf.expand_dims( reader.get_image(FLAGS.content_image, FLAGS.image_size, FLAGS.image_size, image_preprocessing_fn), 0) # image = tf.expand_dims( # _reader.get_image(FLAGS.content_image, FLAGS.image_size), 0) _, endpoints = network_fn(image, spatial_squeeze=False) layers = [] for layer in content_layers: layers.append(endpoints[layer]) with tf.Session() as sess: init_func = utils._get_init_fn(FLAGS) init_func(sess) return sess.run(layers + [image]) def main(argv=None): # style_features_t = losses.get_style_features(FLAGS) # Make sure the training path exists. training_path = os.path.join(FLAGS.model_path, FLAGS.naming) if not(os.path.exists(training_path)): os.makedirs(training_path) """get features""" style_features_t = get_style_features(FLAGS.style_image, FLAGS.style_layers) res = get_content_features(FLAGS.content_image, FLAGS.content_layers) content_features_t, image_t = res[:-1], res[-1] image = tf.constant(image_t) random = tf.random_normal(image_t.shape) initial = tf.Variable(image) """Build Network""" network_fn = nets_factory.get_network_fn( FLAGS.loss_model, num_classes=1, is_training=True) image_preprocessing_fn, image_unprocessing_fn = preprocessing_factory.get_preprocessing( FLAGS.loss_model, is_training=False) preprocess_content_image = tf.expand_dims( reader.get_image(FLAGS.content_image, FLAGS.image_size, FLAGS.image_size, image_preprocessing_fn), 0) # preprocess_content_image = tf.expand_dims( # _reader.get_image(FLAGS.content_image, FLAGS.image_size), 0) # preprocess_style_image = tf.expand_dims( # reader.get_image(FLAGS.style_image, FLAGS.image_size, FLAGS.image_size, image_preprocessing_fn), 0) _, endpoints_dict = network_fn(preprocess_content_image, spatial_squeeze=False) """build loss""" content_loss = 0 for content_features, layer in zip(content_features_t, FLAGS.content_layers): layer_size = tf.size(content_features) content_loss += tf.nn.l2_loss(endpoints_dict[layer] - content_features) / tf.to_float(layer_size) content_loss = FLAGS.content_weight * content_loss / len(FLAGS.content_layers) style_loss = 0 for style_gram, layer in zip(style_features_t, FLAGS.style_layers): layer_size = tf.size(style_gram) style_loss += tf.nn.l2_loss(gram(endpoints_dict[layer]) - style_gram) / tf.to_float(layer_size) # style_loss += (gram(endpoints_dict[layer]) - style_gram) style_loss = FLAGS.style_weight * style_loss tv_loss = FLAGS.tv_weight * total_variation_loss(initial) total_loss = content_loss + style_loss + tv_loss train_op = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(total_loss) output_image = tf.image.encode_png(tf.saturate_cast(tf.squeeze(initial) + reader.mean_pixel, tf.uint8)) with tf.Session() as sess: init_func = utils._get_init_fn(FLAGS) init_func(sess) sess.run(tf.global_variables_initializer()) start_time = time.time() for step in range(FLAGS.step): _, loss_t, cl, sl = sess.run([train_op, total_loss, content_loss, style_loss]) elapsed = time.time() - start_time start_time = time.time() print(step, elapsed, loss_t, cl, sl) image_t = sess.run(output_image) with open('out.png', 'wb') as f: f.write(image_t) if __name__ == '__main__': tf.app.run()

[ "412214410@qq.com" ]

412214410@qq.com

70ddfb5469533e9612ff63f5df784bca6e0d927f

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/examples/cifar10_cnn.py

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[ "MIT" ]

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seba-1511/gsoc15-demo

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refs/heads/master

2021-01-18T14:34:28.686048

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from keras.datasets import cifar10 from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation, Flatten from keras.layers.convolutional import Convolution2D, MaxPooling2D from keras.optimizers import SGD, Adadelta, Adagrad from keras.utils import np_utils, generic_utils ''' Train a (fairly simple) deep CNN on the CIFAR10 small images dataset. GPU run command: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python cifar10_cnn.py It gets down to 0.65 test logloss in 25 epochs, and down to 0.55 after 50 epochs. (it's still underfitting at that point, though). ''' batch_size = 32 nb_classes = 10 nb_epoch = 25 data_augmentation = True # the data, shuffled and split between tran and test sets (X_train, y_train), (X_test, y_test) = cifar10.load_data(test_split=0.1) print X_train.shape[0], 'train samples' print X_test.shape[0], 'test samples' # convert class vectors to binary class matrices Y_train = np_utils.to_categorical(y_train, nb_classes) Y_test = np_utils.to_categorical(y_test, nb_classes) model = Sequential() model.add(Convolution2D(32, 3, 3, 3, border_mode='full')) model.add(Activation('relu')) model.add(Convolution2D(32, 32, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(poolsize=(2, 2))) model.add(Dropout(0.25)) model.add(Convolution2D(64, 32, 3, 3, border_mode='full')) model.add(Activation('relu')) model.add(Convolution2D(64, 64, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(poolsize=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten(64*8*8)) model.add(Dense(64*8*8, 512, init='normal')) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(512, nb_classes, init='normal')) model.add(Activation('softmax')) # let's train the model using SGD + momentum (how original). sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd) if not data_augmentation: print "Not using data augmentation or normalization" X_train = X_train.astype("float32") X_test = X_test.astype("float32") X_train /= 255 X_test /= 255 model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=10) score = model.evaluate(X_test, Y_test, batch_size=batch_size) print 'Test score:', score else: print "Using real time data augmentation" # this will do preprocessing and realtime data augmentation datagen = ImageDataGenerator( featurewise_center=True, # set input mean to 0 over the dataset samplewise_center=False, # set each sample mean to 0 featurewise_std_normalization=True, # divide inputs by std of the dataset samplewise_std_normalization=False, # divide each input by its std zca_whitening=False, # apply ZCA whitening rotation_range=20, # randomly rotate images in the range (degrees, 0 to 180) width_shift_range=0.2, # randomly shift images horizontally (fraction of total width) height_shift_range=0.2, # randomly shift images vertically (fraction of total height) horizontal_flip=True, # randomly flip images vertical_flip=False) # randomly flip images # compute quantities required for featurewise normalization # (std, mean, and principal components if ZCA whitening is applied) datagen.fit(X_train) for e in range(nb_epoch): print '-'*40 print 'Epoch', e print '-'*40 print "Training..." # batch train with realtime data augmentation progbar = generic_utils.Progbar(X_train.shape[0]) for X_batch, Y_batch in datagen.flow(X_train, Y_train): loss = model.train(X_batch, Y_batch) progbar.add(X_batch.shape[0], values=[("train loss", loss)]) print "Testing..." # test time! progbar = generic_utils.Progbar(X_test.shape[0]) for X_batch, Y_batch in datagen.flow(X_test, Y_test): score = model.test(X_batch, Y_batch) progbar.add(X_batch.shape[0], values=[("test loss", score)])

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seba-1511@hotmail.com

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/src/plt_roc.py

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Sapphirine/Analysis-on-Children-Learning-Performance

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import os from src.model import model def clear_temp(): for i in range(1, 4): folder_name = 'result_' + str(i) file_list = [f for f in os.listdir("static/temp/" + folder_name + '/') if f.endswith(".png")] for f in file_list: os.remove("static/temp/" + folder_name + '/' + f) def create_pic(test_num, names, model_name): if not names[model_name]: name = '1' else: name = str(max(names[model_name]) + 1) folder_name = 'result_' + model_name[-1] if model_name[-1] == '1': score = model.predict(test_num, 1, folder_name, name) elif model_name[-1] == '2': score = model.predict(test_num, 2, folder_name, name) elif model_name[-1] == '3': score = model.predict(test_num, 3, folder_name, name) return name, score

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/gbd_2019/shared_code/central_comp/cod/codem/codem/data/query.py

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[]

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Nermin-Ghith/ihme-modeling

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""" Functions to query the causes of death database and format and process all of the CODEm input data. Needs helper functions from the demographics, shared, and covariates modules. """ import pandas as pd import numpy as np import sqlalchemy as sql import logging import pymysql import re import codem.data.queryStrings as QS import codem.reference.db_connect as db_connect from codem.data.demographics import get_mortality_data from codem.data.shared import get_location_info, exclude_regions from codem.data.covariates import get_all_covariates logger = logging.getLogger(__name__) def save_model_outliers(model_version_id, gbd_round_id, decomp_step_id, connection): """ Execute any stored procedure in the specified database with a list of arguments. :param model_version_id: int model version ID :param gbd_round_id: int gbd round ID :param decomp_step_id: int decomposition step ID :param connection: str database that you wish to execute the stored procedure on :return: None """ logger.info('Running outlier stored procedure.') creds = db_connect.read_creds() db = 'ADDRESS'.format(creds=creds, connection=connection) engine = sql.create_engine(db) connect = engine.raw_connection() cursor = connect.cursor() try: cursor.callproc('cod.save_model_outliers', [ float(model_version_id), float(gbd_round_id), float(decomp_step_id) ]) except pymysql.err.InternalError as e: code, msg = e.args if re.search('No outlier found for this model version id', str(msg)): logger.info('There are no outliers for the model version ID {}'.format(model_version_id)) else: if re.search('already exists in outlier_history table', str(msg)): logger.info('Model version ID {} already exists in the outlier_history table.'.format(model_version_id)) else: raise e finally: cursor.close() connect.commit() def copy_model_outliers(old_model_version_id, new_model_version_id, connection): """ Execute any stored procedure in the specified database with a list of arguments. :param old_model_version_id: int old "from" model version ID :param new_model_version_id: int new "to" model version ID :param connection: str database that you wish to execute the stored procedure on :return: None """ logger.info('Running outlier stored copy procedure for old model versions..') creds = db_connect.read_creds() db = 'ADDRESS'.format(creds=creds, connection=connection) engine = sql.create_engine(db) connect = engine.raw_connection() cursor = connect.cursor() try: cursor.callproc('cod.copy_outliers_by_model_version_id', [ float(old_model_version_id), float(new_model_version_id) ]) except pymysql.err.InternalError as e: logger.info("Hit an error with cod.copy_outliers_by_model_version_id.") raise e finally: cursor.close() connect.commit() def exists_in_outlier_history(model_version_id, connection): """ Check to see if this model version already exists in the outlier history table. :param model_version_id: (int) :param connection: (str) :return: """ logger.info(f"Checking to make sure that {model_version_id} does not exist in the outlier history table.") call = f"SELECT COUNT(*) AS count FROM cod.outlier_history WHERE model_version_id = {model_version_id}" count = db_connect.query(call, connection=connection)['count'][0] if count: logger.info(f"The model version {model_version_id} already exists in the outlier history table.") return count def get_cod_data(cause_id, sex, start_year, start_age, end_age, location_set_version_id, refresh_id, outlier_decomp_step_id, db_connection, model_version_id, gbd_round_id, outlier_model_version_id): """ strings indicating model parameters -> Pandas Data Frame Given a list of model parameters will query from the COD database and return a pandas data frame. The data frame contains the base variables used in the CODEm process. Also will call the outlier stored procedure in the database to save model outliers if """ logger.info(f"Querying cod data for refresh {refresh_id} and decomp {outlier_decomp_step_id} outliers.") if not exists_in_outlier_history( model_version_id=model_version_id, connection=db_connection): if model_version_id in outlier_model_version_id: logger.info(f"Running the outlier stored procedure for decomp_step_id {outlier_decomp_step_id}") save_model_outliers( model_version_id=model_version_id, gbd_round_id=gbd_round_id, decomp_step_id=outlier_decomp_step_id, connection=db_connection ) else: for out in outlier_model_version_id: logger.info(f"Running the outlier stored procedure to copy outliers from" f"{out} to {model_version_id}") copy_model_outliers( old_model_version_id=out, new_model_version_id=model_version_id, connection=db_connection ) else: logger.warning("The outlier model version already exists in the table, therefore" "we aren't copying it over.") pass logger.info(f"Querying cod data for refresh {refresh_id}.") call = QS.codQueryStr.format(c=cause_id, s=sex, sy=start_year, sa=start_age, ea=end_age, loc_set_id=location_set_version_id, rv=refresh_id, model_version_id=model_version_id) df = db_connect.query(call, db_connection) df['national'] = df['national'].map(lambda x: x == 1).astype(int) return df def rbinom(n, p, size): """ Wrapper over np binom function that takes nans :param n: int > 0 number of trials :param p: float, 0 < p < 1 probability of success :param size: int > 0 number of observations """ if np.isnan(p) or np.isnan(n): draws = np.repeat(np.nan, size) else: draws = np.random.binomial(n=n, p=p, size=size) return draws def data_variance(df, response): """ (data frame, string) -> array Given a data frame and a response type generates an estimate of the variance for that response based on sample size. A single array is returned where each observation has been sampled 100 times from a normal distribution to find the estimate. """ logger.info("Running data variance for response {}".format(response)) np.seterr(invalid='ignore') cf = df.cf.values n = df.sample_size.values if response == "lt_cf": gc_var = df.gc_var_lt_cf.values elif response == "ln_rate": gc_var = df.gc_var_ln_rate.values else: raise RuntimeError("Must specify lt_cf or ln_rate!") env = df.envelope.values pop = df["pop"].values cf[cf <= 0.00000001] = np.NaN cf[cf >= 1.] = np.NaN cf_sd = (cf * (1-cf) / n)**.5 cf_sd[cf_sd > .5] = .5 # cap cf_sd f = lambda i: np.random.normal(cf[i], cf_sd[i], 100) * (env[i]/pop[i]) if response == "lt_cf": f = lambda i: np.random.normal(cf[i], cf_sd[i], 100) draws = np.array(list(map(f, range(len(cf))))) draws[draws <= 0] = np.NaN if response == "lt_cf": draws = np.log(draws / (1 - draws)) elif response == "ln_rate": draws = np.log(draws) draws_masked = np.ma.masked_array(draws, np.isnan(draws)) ss_var = np.array(draws_masked.var(axis=1)) sd_final = (ss_var + gc_var) ** 0.5 sd_final[sd_final == 0.] = np.NaN np.seterr(invalid='warn') return sd_final def data_process(df): """ Pandas data frame -> Pandas data frame Given a pandas data frame that was queried for CODEm returns a Pandas data frame that has columns added for mixed effect analysis and is re-indexed after removing countries with full sub-national data. """ df2 = df.copy() remove = df2[(df.is_estimate == 0) & (df.standard_location == 0)].country_id.unique() df2 = df2[df2.location_id.map(lambda x: x not in remove)] df2 = df2.replace([np.inf, -np.inf], np.nan) df2["region_nest"] = df2.super_region.map(str) + ":" + df2.region.map(str) df2["age_nest"] = df2.region_nest + ":" + df2.age.map(str) df2["country_nest"] = df2.region_nest + ":" + df2.country_id.map(str) df2["sub_nat_nest"] = df2.country_nest + ":" + df2.location_id.map(str) df2["ln_rate_sd"] = data_variance(df2, "ln_rate") df2["lt_cf_sd"] = data_variance(df2, "lt_cf") df2.reset_index(inplace=True, drop=True) return df2 def get_codem_data(cause_id, sex, start_year, start_age, end_age, regions_exclude, location_set_version_id, decomp_step_id, refresh_id, gbd_round, db_connection, model_version_id, gbd_round_id, env_run_id, pop_run_id, outlier_model_version_id, outlier_decomp_step_id, standard_location_set_version_id): """ :param cause_id: int cause_id to pull results from :param sex: int, 1 or 2 sex_id to query :param start_year: int year of first data point :param start_age: int age of first data point :param end_age: int age of last data point :param regions_exclude: str str of regions to exclude :param location_set_version_id: int cod location version to use :param decomp_step_id: int integer 1-5 that indicates which step of the decomposition analysis (for pulling outliers) :param refresh_id: int refresh ID to use to pull cod.cv_data :param db_connection: str db connection string not including .ihme.forecasting.edu :param model_version_id: int model version of the CODEm model :param gbd_round_id: int GBD round ID :param gbd_round: int year round that we are working with :param pop_run_id: int run ID for get_population :param env_run_id: int run ID for get_envelope :param outlier_model_version_id: int which model version to use for outliers :param outlier_decomp_step_id: int which outliers to pull for those that are pulling active outliers :param standard_location_set_version_id: int standard location set version ID to use :return: data frame data frame with all model data """ logger.info("Beginning full CoD query.") cod = get_cod_data( cause_id=cause_id, sex=sex, start_year=start_year, start_age=start_age, end_age=end_age, location_set_version_id=location_set_version_id, refresh_id=refresh_id, outlier_decomp_step_id=outlier_decomp_step_id, db_connection=db_connection, model_version_id=model_version_id, gbd_round_id=gbd_round_id, outlier_model_version_id=outlier_model_version_id ) mort = get_mortality_data( sex=sex, start_year=start_year, start_age=start_age, end_age=end_age, location_set_version_id=location_set_version_id, gbd_round_id=gbd_round_id, gbd_round=gbd_round, decomp_step_id=decomp_step_id, db_connection=db_connection, pop_run_id=pop_run_id, env_run_id=env_run_id, standard_location_set_version_id=standard_location_set_version_id ) loc = get_location_info(location_set_version_id, standard_location_set_version_id=standard_location_set_version_id, db_connection=db_connection) loc = exclude_regions(loc, regions_exclude=regions_exclude) mort_df = mort.merge(loc, how='right', on=['location_id']) cod_df = cod.merge(mort_df, how='right', on=['location_id', 'age', 'sex', 'year']) cod_df.loc[cod_df["cf"] == 1, "cf"] = np.NAN cod_df.loc[cod_df["cf"] == 0, "cf"] = np.NAN cod_df['ln_rate'] = np.log(cod_df['cf'] * cod_df['envelope'] / cod_df['pop']) cod_df['lt_cf'] = np.log(cod_df['cf'].map(lambda x: x/(1.0-x))) df = data_process(cod_df) return df def get_codem_input_data(model_parameters): """ Given an integer which represents a valid model version ID, returns two pandas data frames. The first is the input data needed for running CODEm models and the second is a data frame of meta data needed for covariate selection. :param model_parameters: dictionary of model parameters """ df = get_codem_data( cause_id=model_parameters["cause_id"], sex=model_parameters["sex_id"], start_year=model_parameters["start_year"], start_age=model_parameters["age_start"], end_age=model_parameters["age_end"], regions_exclude=model_parameters["locations_exclude"], location_set_version_id=model_parameters["location_set_version_id"], decomp_step_id=model_parameters["decomp_step_id"], refresh_id=model_parameters["refresh_id"], db_connection=model_parameters["db_connection"], gbd_round=model_parameters["gbd_round"], model_version_id=model_parameters["model_version_id"], gbd_round_id=model_parameters["gbd_round_id"], env_run_id=model_parameters["env_run_id"], pop_run_id=model_parameters["pop_run_id"], outlier_model_version_id=model_parameters["outlier_model_version_id"], outlier_decomp_step_id=model_parameters['outlier_decomp_step_id'], standard_location_set_version_id=model_parameters["standard_location_set_version_id"] ) cov_df, priors = get_all_covariates( model_version_id=model_parameters["model_version_id"], sex=model_parameters["sex_id"], decomp_step_id=model_parameters["decomp_step_id"], gbd_round_id=model_parameters["gbd_round_id"], location_set_version_id=model_parameters["location_set_version_id"], db_connection=model_parameters["db_connection"], standard_location_set_version_id=model_parameters["standard_location_set_version_id"] ) df = df[ (df.year >= model_parameters["start_year"]) & (df.age >= model_parameters["age_start"]) & (df.age <= model_parameters["age_end"]) ] df2 = df.merge(cov_df, how="left", on=["location_id", "age", "sex", "year"]) covs = df2[priors.name.values] df = df.drop_duplicates() covs = covs.loc[df.index] df.reset_index(drop=True, inplace=True) covs.reset_index(drop=True, inplace=True) columns = df.columns.values[df.dtypes.values == np.dtype('float64')] df[columns] = df[columns].astype('float32') return df, covs, priors def adjust_input_data(df, covs): """ Adjust the input data such that observations with missing covariates, or the envelope/population are equal to zero. Also change cf values of zero to NaN """ logger.info("Adjusting input data.") # remove observations where covariate values are missing adjust_df = df.copy() covariates = covs.copy() if covariates.isnull().values.any(): raise RuntimeError("You have null covariates!") covariates.dropna(inplace=True) adjust_df.drop(np.setdiff1d(adjust_df.index.values, covariates.index.values), inplace=True) # remove observations where population or envelope is zero zeroes = adjust_df[(adjust_df["envelope"] <= 0) | (adjust_df["pop"] <= 0)] if not zeroes.empty: raise RuntimeError("You have negative or 0 envelope/pops!") adjust_df = adjust_df[(adjust_df["envelope"] > 0) & (adjust_df["pop"] > 0)] covariates.drop(np.setdiff1d(covariates.index.values, adjust_df.index.values), inplace=True) # change cf values of zero and one in the main data frame to np.NaN adjust_df["cf"] = adjust_df["cf"].map(lambda x: np.NaN if x <= 0.00000001 or x >= 1 else x) adjust_df["cf"][(adjust_df["lt_cf_sd"].isnull()) | (adjust_df["ln_rate_sd"].isnull())] = np.NaN adjust_df["ln_rate"][(adjust_df["cf"].isnull())] = np.NaN adjust_df["lt_cf"][(adjust_df["cf"].isnull())] = np.NaN covariates.reset_index(drop=True, inplace=True) adjust_df.reset_index(drop=True, inplace=True) return adjust_df, covariates, pd.concat([adjust_df, covariates], axis=1)

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cheth@uw.edu

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import os import mimetypes import zipfile from dateutil.tz import * from . import iron_core try: import json except ImportError: import simplejson as json def file_exists(file): """Check if a file exists.""" if not os.path.exists(file): return False try: open(file) except IOError: return False return True class Task: id = None project = None code_id = None code_history_id = None status = None code_name = None code_rev = None created_at = None updated_at = None start_time = None end_time = None duration = None timeout = 3600 message = None delay = 0 start_at = None end_at = None next_start = None last_run_time = None run_times = None run_count = None run_every = None percent = None payload = None priority = 0 scheduled = False repeating = False __json_attrs = ["payload"] __rfc3339_attrs = [ "created_at", "updated_at", "start_at", "end_at", "next_start", "last_run_time"] __timestamp_attrs = ["start_time", "end_time"] __schedule_attrs = [ "start_at", "end_at", "next_start", "last_run_time", "run_count", "run_every"] __repeating_attrs = ["end_at", "next_start", "run_every"] __aliases = { "project": "project_id", "msg": "message" } __ignore = ["message"] def __str__(self): if self.id is not None and self.scheduled: return "IronWorker Scheduled Task #%s" % self.id elif self.id is not None: return "IronWorker Task #%s" % self.id else: return "IronWorker Task" def __repr__(self): return "<%s>" % str(self) def __set(self, attr, value): if attr in self.__rfc3339_attrs: if isinstance(value, basestring): value = iron_core.IronClient.fromRfc3339(value) if attr in self.__schedule_attrs: self.scheduled = True if attr in self.__repeating_attrs: self.repeating = True if attr in self.__json_attrs: if isinstance(value, basestring): try: value = json.loads(value) except: pass setattr(self, attr, value) def __init__(self, values=None, **kwargs): if values is None: values = {} self.payload = {} attrs = [ x for x in vars(self.__class__).keys() if not x.startswith("__")] for k in kwargs.keys(): values[k] = kwargs[k] for prop in values.keys(): if prop in attrs and prop not in self.__ignore: self.__set(prop, values[prop]) elif prop in self.__aliases: self.__set(self.__aliases[prop], values[prop]) class CodePackage: id = None project = None name = None runtime = None latest_checksum = None revision = None latest_history_id = None latest_change = None files = None executable = None zip_path = None __rfc3339_attrs = ["latest_change"] __aliases = { "project_id": "project", "rev": "revision", "exec": "executable" } def __str__(self): if self.name is not None: return "%s Code Package" % self.name elif self.id is not None: return "Code Package #%s" % self.id else: return "IronWorker Code Package" def __repr__(self): return "<%s>" % str(self) def __set(self, attr, value): if attr in self.__rfc3339_attrs: value = iron_core.IronClient.fromRfc3339(value) setattr(self, attr, value) def __init__(self, values=None, **kwargs): if values is None: values = {} self.files = {} for k in kwargs.keys(): values[k] = kwargs[k] attrs = [ x for x in vars(self.__class__).keys() if not x.startswith("__")] for prop in values.keys(): if prop in attrs: self.__set(prop, values[prop]) elif prop in self.__aliases: self.__set(self.__aliases[prop], values[prop]) def merge(self, target, ignoreRootDir=False): if os.path.isfile(target): self.files[os.path.basename(target)] = target elif os.path.isdir(target): for dirname, dirnames, filenames in os.walk(target): for filename in filenames: path = os.path.join(dirname, filename) if ignoreRootDir: ziploc = path.lstrip(target).lstrip("/") else: ziploc = path self.files[ziploc] = path else: raise ValueError("'%s' is not a file or directory." % target) if len(self.files) == 1: for dest, loc in self.files.iteritems(): self.executable = dest def merge_dependency(self, dep): dependency = __import__(dep) location = os.path.dirname(dependency.__file__) parent = location.rstrip(os.path.basename(location)) for dirname, dirnames, filenames in os.walk(location): for filename in filenames: path = os.path.join(dirname, filename) if path.startswith(parent): newpath = path[len(parent):] else: newpath = path ziploc = newpath.lstrip("/") self.files[ziploc] = path def zip(self, destination=None, overwrite=True): if destination is None: if self.name is not None: destination = "%s.zip" % self.name else: raise ValueError("Package name or destination is required.") if file_exists(destination) and not overwrite: raise ValueError("Destination '%s' already exists." % destination) filelist = self.files.copy() for dest, loc in filelist.items(): if not file_exists(loc): del(self.files[dest]) if len(self.files) > 0: z = zipfile.ZipFile(destination, "w") for dest, loc in self.files.items(): z.write(loc, dest) z.close() self.zip_path = destination return file_exists(destination) class IronWorker: NAME = "iron_worker_python" VERSION = "1.2.0" def __init__(self, **kwargs): """Prepare a configured instance of the API wrapper and return it. Keyword arguments are passed directly to iron_core_python; consult its documentation for a full list and possible values.""" self.client = iron_core.IronClient( name=IronWorker.NAME, version=IronWorker.VERSION, product="iron_worker", **kwargs) ############################################################# ####################### CODE PACKAGES ####################### ############################################################# def codes(self): packages = [] resp = self.client.get("codes") raw_packages = resp["body"]["codes"] for package in raw_packages: packages.append(CodePackage(package)) return packages def code(self, id): if isinstance(id, CodePackage): id = id.id resp = self.client.get("codes/%s" % id) raw_package = resp["body"] return CodePackage(raw_package) def postCode(self, code, zipFilename=None): zip_loc = code.zip_path if zipFilename is not None: zip_loc = zipFilename if zip_loc is None: raise ValueError("Need to set the zip file to upload.") if not file_exists(zip_loc): raise ValueError("File doesn't exist: %s" % zip_loc) if code.name is None: raise ValueError("Code needs a name.") if code.executable is None: raise ValueError("Code's executable file needs to be set.") if code.runtime is None: code.runtime = "python" file = open(zip_loc, "rb") file_contents = file.read() file.close() data = [("data", json.dumps({ "name": code.name, "runtime": code.runtime, "file_name": code.executable }))] files = [("file", zip_loc, file_contents)] content_type, body = IronWorker.encode_multipart_formdata(data, files) headers = { "Content-Type": content_type } resp = self.client.post(url="codes", body=body, headers=headers) return CodePackage(resp["body"]) def upload(self, target, name=None, executable=None, overwrite=True): if isinstance(target, CodePackage): code = target else: code = CodePackage() code.merge(target) if name is not None: code.name = name if executable is not None: code.executable = executable if code.name is None: raise ValueError("Need to set a name for the package.") if code.executable is None: raise ValueError("Need to set a file as the executable.") clean_up = not file_exists("%s.zip" % code.name) or overwrite if code.zip_path is None or not file_exists(code.zip_path): code.zip(overwrite=overwrite) result = self.postCode(code) if clean_up: os.remove(code.zip_path) return result def deleteCode(self, id): if isinstance(id, CodePackage): id = id.id self.client.delete("codes/%s" % id) return True def revisions(self, id): revisions = [] if isinstance(id, CodePackage): id = id.id resp = self.client.get("codes/%s/revisions" % id) raw_revs = resp["body"]["revisions"] for rev in raw_revs: revisions.append(CodePackage(rev)) return revisions def download(self, id, rev=None, destination=None): if isinstance(id, CodePackage): if rev is None and id.revision is not None: rev = id.revision id = id.id url = "codes/%s/download" % id if rev is not None: url = "%s?revision=%s" % (url, rev) resp = self.client.get(url) dest = resp["resp"].getheader("Content-Disposition") dest = dest.lstrip("filename=") if destination is not None: if os.path.isdir(destination): dest = os.path.join(destination, dest) else: dest = destination dup_dest = dest iteration = 1 while file_exists(dup_dest) and destination is None: iteration += 1 dup_dest = dest.rstrip(".zip") + " (" + str(iteration) + ").zip" f = open(dup_dest, "wb") f.write(resp["body"]) f.close() return file_exists(dup_dest) ############################################################# ########################## TASKS ############################ ############################################################# def tasks(self, scheduled=False): tasks = [] if not scheduled: resp = self.client.get("tasks") raw_tasks = resp["body"] raw_tasks = raw_tasks["tasks"] else: resp = self.client.get("schedules") raw_tasks = resp["body"] raw_tasks = raw_tasks["schedules"] for raw_task in raw_tasks: tasks.append(Task(raw_task)) return tasks def queue(self, task=None, tasks=None, retry=None, **kwargs): tasks_data = [] if task is None: task = Task(**kwargs) if tasks is None: tasks = [task] for task in tasks: payload = task.payload if not isinstance(payload, basestring): payload = json.dumps(payload) if task.code_name is None: raise ValueError("task.code_name is required.") task_data = { "name": task.code_name, "code_name": task.code_name, "payload": payload, "priority": task.priority, "delay": task.delay } if not task.scheduled: type_str = "tasks" task_data["timeout"] = task.timeout else: type_str = "schedules" if task.run_every is not None: task_data["run_every"] = task.run_every if task.end_at is not None: if task.end_at.tzinfo is None: task.end_at = task.end_at.replace(tzinfo=tzlocal()) task_data["end_at"] = iron_core.IronClient.toRfc3339(task.end_at) if task.run_times is not None: task_data["run_times"] = task.run_times if task.start_at is not None: if task.start_at.tzinfo is None: task.start_at = task.start_at.replace(tzinfo=tzlocal()) task_data["start_at"] = iron_core.IronClient.toRfc3339(task.start_at) tasks_data.append(task_data) data = json.dumps({type_str: tasks_data}) headers = {"Content-Type": "application/json"} if retry is not None: resp = self.client.post(type_str, body=data, headers=headers, retry=retry) else: resp = self.client.post(type_str, body=data, headers=headers) tasks = resp["body"] if len(tasks[type_str]) > 1: return [Task(task, scheduled=(type_str == "schedules")) for task in tasks[type_str]] else: return Task(tasks[type_str][0], scheduled=(type_str == "schedules")) def task(self, id, scheduled=False): if isinstance(id, Task): scheduled = id.scheduled id = id.id if not scheduled: url = "tasks/%s" % id else: url = "schedules/%s" % id resp = self.client.get(url) raw_task = resp["body"] return Task(raw_task) def log(self, id): if isinstance(id, Task): if id.scheduled: raise ValueError("Cannot retrieve a scheduled task's log.") id = id.id url = "tasks/%s/log" % id headers = {"Accept": "text/plain"} resp = self.client.get(url, headers=headers) return resp["body"] def cancel(self, id, scheduled=False): if isinstance(id, Task): scheduled = id.scheduled id = id.id if not scheduled: url = "tasks/%s/cancel" % id else: url = "schedules/%s/cancel" % id self.client.post(url) return True ############################################################# ######################### HELPERS ########################### ############################################################# @staticmethod def encode_multipart_formdata(fields, files): """ fields is a sequence of (name, value) elements for regular form fields. files is a sequence of (name, filename, value) elements for data to be uploaded as files Return (content_type, body) ready for httplib.HTTP instance """ BOUNDARY = '----------ThIs_Is_tHe_bouNdaRY_$' CRLF = '\r\n' L = [] for (key, value) in fields: L.append('--' + BOUNDARY) L.append('Content-Disposition: form-data; name="%s"' % key) L.append('') L.append(value) for (key, filename, value) in files: L.append('--' + BOUNDARY) L.append('Content-Disposition: form-data; name="%s"; filename="%s"' % (key, filename)) L.append('Content-Type: %s' % IronWorker.get_content_type(filename)) L.append('') L.append(value) L.append('--' + BOUNDARY + '--') L.append('') body = CRLF.join(L) content_type = 'multipart/form-data; boundary=%s' % BOUNDARY return content_type, str(body) @staticmethod def get_content_type(filename): return mimetypes.guess_type(filename)[0] or 'application/octet-stream'

[ "jack.thatch@gmail.com" ]

jack.thatch@gmail.com

70184ab14338c2a28726a4e83e4412be6ed0cadb

6df76f8a6fcdf444c3863e3788a2f4b2c539c22c

/django code/p41/p41/urls.py

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[]

no_license

basantbhandari/DjangoProjectsAsDocs

068e4a704fade4a97e6c40353edb0a4299bd9678

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refs/heads/master

2022-12-18T22:33:23.902228

2020-09-22T13:11:01

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py

"""p41 URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include urlpatterns = [ path('admin/', admin.site.urls), path('enroll/', include('enroll.urls')) ]

[ "36443209+basantbhandari@users.noreply.github.com" ]

36443209+basantbhandari@users.noreply.github.com

2435312c8826faf2062cab3201f18f92c06d0d66

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/synaptor/seg_utils/relabel.py

91343bf2a92925a627505864733973550b225989

[ "MIT" ]

permissive

nkemnitz/Synaptor

b60f33e51ed045e7cdaf18465af3d80edaca1cf3

40618786d5b762eb3877ecac49ff310f3e6f892d

refs/heads/master

2020-05-06T15:43:38.584607

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import numpy as np from . import describe from . import _relabel def relabel_data(d, mapping, copy=True): """ Relabel data according to a mapping dict. Modify the entries of :param:d according to a :param:mapping dictionary. If a value within :param:d doesn't match a key for :param:mapping, leave it unchanged. Args: d (3darray): A data volume. mapping (dict): A mapping from data values in d to new desired values. copy (bool): Whether or not to perform relabeling in-place. Defaults to True, which will create a new volume. Returns: 3darray: A modified or newly created volume with the desired modifications. """ if copy: d = np.copy(d) return _relabel.relabel_data(d, mapping) def relabel_data_1N(d, copy=True): """ Relabel segment values from 1:N Args: d (3darray): A segmentation. copy (bool): Whether or not to perform relabeling in-place. Defaults to True, which will create a new volume. Returns: 3darray: A modified or newly created volume with new segids. """ mapping = {v: i+1 for (i, v) in enumerate(describe.nonzero_unique_ids(d))} return relabel_data(d, mapping, copy=copy) def relabel_data_iterative(d, mapping): """ Python-based iterative relabeling Remapping data according to an id mapping using an iterative strategy. Best when only modifying a few ids. If a value within d doesn't match a key for mapping, leave it unchanged. Args: d (3darray): A segmentation. mapping (dict): A mapping from data values in d to new desired values. Returns: 3darray: A new volume with the desired modifications. """ r = np.copy(d) src_ids = set(np.unique(d)) mapping = dict(filter(lambda x: x[0] in src_ids, mapping.items())) for (k, v) in mapping.items(): r[d == k] = v return r def relabel_data_lookup_arr(d, mapping): """ Python-based lookup array relabeling Remapping data according to an id mapping using a lookup np array. Best when modifying several ids at once and ids are approximately dense within 1:max Args: d (3darray): A segmentation. mapping (dict): A mapping from data values in d to new desired values. Returns: 3darray: A new volume with the desired modifications. """ if len(mapping) == 0: return d map_keys = np.array(list(mapping.keys())) map_vals = np.array(list(mapping.values())) map_arr = np.arange(0, d.max()+1) map_arr[map_keys] = map_vals return map_arr[d]

[ "nturner.stanford@gmail.com" ]

nturner.stanford@gmail.com

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/app/error.py

bd23e12d9065af85513cc0e57d88c9b0ff65e2c4

[]

no_license

dicksonkariuki/Watchlist

47cf68c45d1ecd810c986a12cb8934ab8453e09c

2089a577ff6b8bf07d14232658ce9671b6ebb899

refs/heads/master

2020-08-08T19:03:24.766702

2019-10-17T07:28:38

213,634,803

0

null

UTF-8

Python

false

205

py

from flask import render_template from app import app @app.errorhandler(404) def four_Ow_four(error): """ Function to render the 404 page """ return render_template ('fourOwfour.html'),404

[ "dicksonkariuki4@gmail.com" ]

dicksonkariuki4@gmail.com

d9b3012794241a6b430ddc7807eaaf0d74e8c56f

d8ea695288010f7496c8661bfc3a7675477dcba0

/examples/raspberry_pi/relay.py

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[]

no_license

dabolau/demo

de9c593dabca26144ef8098c437369492797edd6

212f4c2ec6b49baef0ef5fcdee6f178fa21c5713

refs/heads/master

2021-01-17T16:09:48.381642

2018-10-08T10:12:45

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py

import RPi.GPIO as GPIO import time def relay(i=0): # 设置针脚模式为（BOARD） GPIO.setmode(GPIO.BOARD) # 禁用警告 GPIO.setwarnings(False) # 设置针脚 PIN = 40 # 设置针脚为输出模式 GPIO.setup(PIN, GPIO.OUT) # 设置开关（0/1），0表示关，1表示开。 INT = i # 开（闭合） if INT == 1: GPIO.output(PIN, GPIO.HIGH) # 高电平输出 print('power on') # 关（断开） if INT == 0: GPIO.output(PIN, GPIO.LOW) # 低电平输出 print('power off') # 延时5秒 time.sleep(5) # 释放针脚 GPIO.cleanup() if __name__ == '__main__': relay(1) # 开 relay(0) # 关 relay(1) # 开

[ "dabolau@qq.com" ]

dabolau@qq.com

c6eb011206a6832c3dd908dc6cb075ac850cb450

8f3336bbf7cd12485a4c52daa831b5d39749cf9b

/Python/binary-tree-right-side-view.py

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[]

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black-shadows/LeetCode-Topicwise-Solutions

9487de1f9a1da79558287b2bc2c6b28d3d27db07

b1692583f7b710943ffb19b392b8bf64845b5d7a

refs/heads/master

2022-05-30T22:16:38.536678

2022-05-18T09:18:32

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2020-05-08T13:04:36

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C++

UTF-8

Python

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py

# Time: O(n) # Space: O(h) class TreeNode(object): def __init__(self, x): self.val = x self.left = None self.right = None class Solution(object): # @param root, a tree node # @return a list of integers def rightSideView(self, root): result = [] self.rightSideViewDFS(root, 1, result) return result def rightSideViewDFS(self, node, depth, result): if not node: return if depth > len(result): result.append(node.val) self.rightSideViewDFS(node.right, depth+1, result) self.rightSideViewDFS(node.left, depth+1, result) # BFS solution # Time: O(n) # Space: O(n) class Solution2(object): # @param root, a tree node # @return a list of integers def rightSideView(self, root): if root is None: return [] result, current = [], [root] while current: next_level = [] for node in current: if node.left: next_level.append(node.left) if node.right: next_level.append(node.right) result.append(node.val) current = next_level return result

[ "noreply@github.com" ]

black-shadows.noreply@github.com

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/indices/nntyndal.py

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[]

no_license

psdh/WhatsintheVector

e8aabacc054a88b4cb25303548980af9a10c12a8

a24168d068d9c69dc7a0fd13f606c080ae82e2a6

refs/heads/master

2021-01-25T10:34:22.651619

2015-09-23T11:54:06

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Python

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py

ii = [('CrokTPS.py', 1), ('WadeJEB.py', 1)]

[ "varunwachaspati@gmail.com" ]

varunwachaspati@gmail.com

be894f7257499ff10f4f259ce795d3033f32e04e

b1f748d761751e89f62cf5b8a2b13adac5bf3a29

/metermaster/urls.py

dcdb41d64262561d4761bce0daa60a73a5e3078d

[]

no_license

sangeeth-subramoniam/buildingmanagementheroku

7b77be693fa73dbd2dff9c816bf50daf1e501029

db26de549f7088d2ff80a303abeeaaa548d43e0b

refs/heads/master

2023-07-08T13:46:06.384694

2021-08-10T06:50:14

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2021-08-04T02:46:57

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Python

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py

from django.urls import path,include from . import views app_name = 'metermaster' urlpatterns = [ path('', views.home , name = "home"), path('metermaster_update_form/<int:pk>', views.updatemeterForm , name = 'updateMeterForm'), path('metermaster_delete_form/<int:pk>', views.deletemeterForm , name = 'deleteMeterForm'), path('ajax/load-stores/', views.load_store, name='ajax_load_stores'), ]

[ "s-sangeeth-k@sicis.co.jp" ]

s-sangeeth-k@sicis.co.jp

1999c84509f04a543cf1c61c698ae75b971dd835

f3ed1631f5cfb10ec3c03974a04f73e1e8dd5829

/handofcats/middlewares/__init__.py

2a4c39ee259addb6db2d9c2c2ba965c6c9a45062

[]

no_license

tell-k/handofcats

9839e20eb3731890a16dcb6d864b7fc13ee80032

135e9abac83db318a7b07337191a1d4f699f7ef2

refs/heads/master

2020-12-25T22:29:35.495296

2016-01-10T00:29:30

null

0

null

UTF-8

Python

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1,111

py

# -*- coding:utf-8 -*- from functools import wraps class MiddlewareApplicator(object): def __init__(self, fns): self.middlewares = [middlewarefy(fn) for fn in fns] def register(self, fn): self.middlewares.append(middlewarefy(fn)) def __call__(self, fn): def call(*args, **kwargs): context = {} context["_args"] = args context["_keys"] = list(kwargs.keys()) context.update(kwargs) def create_result(context): args = context["_args"] kwargs = {k: context[k] for k in context["_keys"]} return fn(*args, **kwargs) closure = create_result for m in reversed(self.middlewares): closure = m(closure) return closure(context) return call def middlewarefy(fn): @wraps(fn) def middleware(closure): return lambda context: fn(context, closure) return middleware from .verbosity_adjustment import middleware_verbosity_adjustment DEFAULT_MIDDLEWARES = [ middleware_verbosity_adjustment, ]

[ "podhmo+altair@beproud.jp" ]

podhmo+altair@beproud.jp

3588b3df70f9fbd1b7167ef3bfa267d162441634

a487691662edb19792007571fc084e68f180af0a

/2020/mapreduceInPython/mapper.py

d3bd22bddd279da6ca99da7d3585b8bd1619f2ba

[]

no_license

eiahb3838ya/PHBS_BigData_2019

a74231817b1114079961c7d4dba8b7adc2794cad

91b71d229188cf750e4acf093615bfba5e27ca96

refs/heads/master

2021-07-15T06:23:43.505842

2020-11-12T03:32:29

225,119,161

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1

null

UTF-8

Python

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# -*- coding: utf-8 -*- """ Created on Wed Nov 4 19:31:24 2020 @author: eiahb """ import sys from multiprocessing import Pool import time def main(): # 读入每行input for line in sys.stdin: aRecord = line.split(",") stockTimeStamp = "{}_{}".format(aRecord[0], aRecord[1][:12]) # results = [] print("%s\t%s" % (stockTimeStamp,aRecord[2])) if __name__ =="__main__": tic = time.time() main() toc = time.time() - tic

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hecanjog/hcj.py

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from pippi import dsp from pippi import tune from hcj import fx midi = {'pc': 3} def play(ctl): param = ctl.get('param') lpd = ctl.get('midi').get('pc') lpd.setOffset(111) key = 'g' #bd = dsp.read('/home/hecanjog/sounds/drums/Tinyrim2.wav').data #bd = dsp.read('/home/hecanjog/sounds/drums/Jngletam.wav').data #bd = dsp.read('/home/hecanjog/sounds/drums/78oh.wav').data #bd = dsp.amp(bd, 1) #bd = dsp.transpose(bd, dsp.rand(0.65, 0.72) / 1) #bd = dsp.transpose(bd, dsp.rand(0.3, 0.32) / 1) chord = tune.fromdegrees([1,8], root='g', octave=dsp.randint(0,2)) chord.reverse() chord = dsp.rotate(chord, lpd.geti(4, low=0, high=len(chord)-1)) #chord = dsp.randshuffle(chord) reps = param.get('reps', default=16) rep = param.get('rep', default=0) beat = dsp.bpm2frames(130) / 4 beat = dsp.mstf(4100) / 32 #length = beat out = '' for n in range(4): freq = chord[int(rep) % len(chord)] if dsp.rand() > 0.5: freq *= 2**dsp.randint(0, lpd.geti(7, low=0, high=8, default=0)) pw = lpd.get(8, low=0.1, high=1, default=1) #length = dsp.mstf(lpd.get(2, low=50, high=2500, default=500) * dsp.rand(0.5, 2)) length = dsp.mstf(lpd.get(14, low=50, high=5000, default=500)) wf = dsp.wavetable('tri', 512) wf = dsp.wavetable('impulse', 512) wf = dsp.wavetable('sine2pi', 512) wf = dsp.breakpoint([0] + [ dsp.rand(-1,1) for w in range(lpd.geti(15, low=4, high=200, default=4)) ] + [0], 512) win = dsp.wavetable('sine', 512) mod = [ dsp.rand(0, 1) for m in range(512) ] modr = dsp.rand(0.01, 0.02) modr = lpd.get(16, low=0.01, high=1, default=1) modf = dsp.rand(0.5, 2) amp = lpd.get(6, low=0, high=2, default=0) amp = dsp.rand(0, 2) o = dsp.pulsar(freq, length, pw, wf, win, mod, modr, modf, amp) o = dsp.env(o, 'random') o = dsp.taper(o, dsp.mstf(10)) o = dsp.pan(o, dsp.rand()) rep = rep + 1 out += o #out = dsp.mix([ dsp.fill(bd, dsp.flen(out), silence=True), out ]) param.set('rep', (rep + 1) % reps) return out

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/codeacademy-python3/base_exponent.py

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ssaulrj/codes-python

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refs/heads/master

2022-11-17T11:40:18.883096

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# Write your large_power function here: def large_power(base, exponent): if base**exponent > 5000: return True else: return False # Uncomment these function calls to test your large_power function: print(large_power(2, 13)) # should print True print(large_power(2, 12)) # should print False

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/04-03/todolist/buy/models.py

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hanifmisbah/tugas_bersama

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from django.db import models # Create your models here. class Buy(models.Model): name = models.TextField(default='') brg = models.TextField(default='') jmlh = models.TextField(default='') price = models.TextField(default='')

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hanifmisbah97@gmail.com

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/python_developer_tools/cv/utils/opencv_utils/视频操作/__init__.py

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# !/usr/bin/env python # -- coding: utf-8 -- # @Author zengxiaohui # Datatime:9/14/2021 4:06 PM # @File:__init__.py

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zengxh@chint.com

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tirkarthi/odin-ai

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from __future__ import absolute_import, division, print_function import numpy as np import tensorflow.compat.v2 as tf from tensorflow_probability.python.bijectors import exp as exp_bijector from tensorflow_probability.python.distributions import ( NegativeBinomial, Normal, QuantizedDistribution, TransformedDistribution, Uniform) from tensorflow_probability.python.internal import dtype_util __all__ = ["qUniform", "qNormal"] class qNormal(QuantizedDistribution): def __init__(self, loc=0., scale=1., min_value=None, max_value=None, validate_args=False, allow_nan_stats=True, name="qNormal"): super(qNormal, self).__init__(distribution=Normal(loc=loc, scale=scale, validate_args=validate_args, allow_nan_stats=allow_nan_stats), low=min_value, high=max_value, name=name) class qUniform(QuantizedDistribution): def __init__(self, low=0., high=1., min_value=None, max_value=None, validate_args=False, allow_nan_stats=True, name="qUniform"): super(qUniform, self).__init__(distribution=Uniform(low=low, high=high, validate_args=validate_args, allow_nan_stats=allow_nan_stats), low=min_value, high=max_value, name=name)

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nickartin13@gmail.com

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/pyquant/marketdata/spot.py

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masa4u/pyquant-xmlrpc

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refs/heads/master

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from pyquant.marketdata.marketdata import MarketDataType from pyquant.marketdata.single import MarketDataSingle class MarketDataSpot(MarketDataSingle): def __init__(self): super(MarketDataSpot, self).__init__() print MarketDataSpot().data_type print MarketDataSpot().value if __name__ == '__main__': from pyquant.marketdata.libor import MarketDataLibor from pyquant.marketdata.cmt import MarketDataCMT from pyquant.marketdata.cms import MarketDataCMS from pyquant.marketdata.curve import MarketDataCurve if issubclass(MarketDataSpot, MarketDataSingle): print 'yes' single_data_list = [MarketDataSpot, MarketDataLibor, MarketDataCMT, MarketDataCurve] for c in single_data_list: print c.__name__, issubclass(c, MarketDataSingle)

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masa4u@gmail.com

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def cents_to_dollars(cents): """ Convert cents to dollars. :param cents: Amount in cents :type cents: int :return: float """ return round(cents / 100.0, 2) def dollars_to_cents(dollars): """ Convert dollars to cents. :param dollars: Amount in dollars :type dollars: float :return: int """ return int(dollars * 100)

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prontomaster@gmail.com

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acckcc/python-training-course

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2022-04-19T17:48:46.699806

2020-04-16T15:04:53

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py

# -*- coding: utf-8 -*- """Unit test package for python_ipify."""

[ "zj0512@gmail.com" ]

zj0512@gmail.com